{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/inference"},"x-facet":{"type":"skill","slug":"inference","display":"Inference","count":100},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_61e346b2-915"},"title":"Sr. Software Engineer, Inference","description":"<p>Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.</p>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP)</li>\n<li>Python or Rust</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p>Representative projects across the org:</p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p>Deadline to apply: None. Applications will be reviewed on a rolling basis.</p>\n<p>The annual compensation range for this role is £225,000-£325,000 GBP.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_61e346b2-915","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5152348008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£225,000-£325,000 GBP","x-skills-required":["High-performance, large-scale distributed systems","Implementing and deploying machine learning systems at scale","Load balancing, request routing, or traffic management systems","LLM inference optimization, batching, and caching strategies","Kubernetes and cloud infrastructure (AWS, GCP)","Python or Rust"],"x-skills-preferred":[],"datePosted":"2026-04-18T16:00:17.377Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"High-performance, large-scale distributed systems, Implementing and deploying machine learning systems at scale, Load balancing, request routing, or traffic management systems, LLM inference optimization, batching, and caching strategies, Kubernetes and cloud infrastructure (AWS, GCP), Python or Rust","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":225000,"maxValue":325000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_f28927b0-573"},"title":"Machine Learning Systems Research Engineer, Agent Post-training - Enterprise GenAI","description":"<p>At Scale, our mission is to accelerate the development of AI applications. We are working on an arsenal of proprietary research and resources that serve all of our enterprise clients. As an ML Sys Research Engineer, you&#39;ll work on building out the algorithms for our next-gen Agent RL training platform, support large scale training, and research and integrate state-of-the-art technologies to optimize our ML system.</p>\n<p>Your customer will be other MLREs and AAIs on the Enterprise AI team who are taking the training algorithms and applying them to client use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models.</p>\n<p>If you are excited about shaping the future of the modern AI movement, we would love to hear from you!</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Build, profile and optimize our training and inference framework.</li>\n<li>Post-train state of the art models, developed both internally and from the community, to define stable post-training recipes for our enterprise engagements.</li>\n<li>Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.</li>\n<li>Create a next-gen agent training algorithm for multi-agent/multi-tool rollouts.</li>\n</ul>\n<p>Ideal Candidate:</p>\n<ul>\n<li>At least 1-3 years of LLM training in a production environment.</li>\n<li>Passionate about system optimization.</li>\n<li>Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.</li>\n<li>Ability to demonstrate know-how on how to operate the architecture of the modern GPU cluster.</li>\n<li>Experience with multi-node LLM training and inference.</li>\n<li>Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.</li>\n<li>Strong written and verbal communication skills to operate in a cross functional team environment.</li>\n<li>PhD or Masters in Computer Science or a related field.</li>\n</ul>\n<p>Compensation:</p>\n<p>We offer competitive compensation packages, including base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.</p>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive health, dental and vision coverage.</li>\n<li>Retirement benefits.</li>\n<li>A learning and development stipend.</li>\n<li>Generous PTO.</li>\n<li>Commuter stipend.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f28927b0-573","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://www.scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4625341005","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$189,600-$237,000 USD","x-skills-required":["LLM training","System optimization","Post-training methods","GPU cluster operation","Multi-node LLM training","Inference","CUDA","Pytorch","Transformers","Flash attention"],"x-skills-preferred":[],"datePosted":"2026-04-18T16:00:01.664Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"LLM training, System optimization, Post-training methods, GPU cluster operation, Multi-node LLM training, Inference, CUDA, Pytorch, Transformers, Flash attention","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":189600,"maxValue":237000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_460d00aa-b48"},"title":"Senior / Staff+ Software Engineer, Voice Platform","description":"<p>About the role</p>\n<p>We&#39;re building the infrastructure that lets people talk to Claude,real-time, bidirectional voice conversations that feel natural, responsive, and safe. This is foundational work for how millions of people will interact with AI.</p>\n<p>The Voice Platform team designs and operates the serving systems, streaming pipelines, and APIs that bring Anthropic&#39;s audio models from research into production across Claude.ai, our mobile apps, and the Anthropic API. You&#39;ll work at the intersection of real-time media, low-latency inference, and distributed systems,building infrastructure where every millisecond of latency is felt by the user.</p>\n<p>We partner closely with the Audio research team, who train the speech understanding and generation models, and with product teams shipping voice experiences to users. Your job is to make those models fast, reliable, and delightful to talk to at scale.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Design and build the real-time streaming infrastructure that powers voice conversations with Claude,ingesting microphone audio, orchestrating model inference, and streaming synthesized speech back with minimal latency</li>\n</ul>\n<ul>\n<li>Build low-latency serving systems for speech models, optimizing time-to-first-audio and end-to-end conversational responsiveness</li>\n</ul>\n<ul>\n<li>Develop the public and internal APIs that expose voice capabilities to Claude.ai, mobile clients, and third-party developers</li>\n</ul>\n<ul>\n<li>Own the audio transport layer,codecs, jitter buffers, adaptive bitrate, packet loss recovery,so conversations stay smooth across unreliable networks</li>\n</ul>\n<ul>\n<li>Build observability and quality-measurement systems for voice: latency distributions, audio quality metrics, interruption handling, and turn-taking accuracy</li>\n</ul>\n<ul>\n<li>Partner with Audio research to move new model architectures from experiment to production, and feed real-world performance data back into research</li>\n</ul>\n<ul>\n<li>Collaborate with mobile and product engineering on client-side audio capture, playback, and the end-to-end user experience</li>\n</ul>\n<p>You may be a good fit if you</p>\n<ul>\n<li>Have 6+ years of experience building distributed systems, real-time infrastructure, or platform services at scale</li>\n</ul>\n<ul>\n<li>Have shipped production systems where latency is measured in tens of milliseconds and users notice when you miss</li>\n</ul>\n<ul>\n<li>Are comfortable working across the stack,from transport protocols and serving infrastructure up to the APIs product teams build on</li>\n</ul>\n<ul>\n<li>Are results-oriented, with a bias toward flexibility and impact</li>\n</ul>\n<ul>\n<li>Pick up slack, even if it goes outside your job description</li>\n</ul>\n<ul>\n<li>Enjoy pair programming (we love to pair!)</li>\n</ul>\n<ul>\n<li>Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly</li>\n</ul>\n<ul>\n<li>Are comfortable with ambiguity,voice is a fast-moving space, and you&#39;ll help define the architecture as we learn what works</li>\n</ul>\n<p>Strong candidates may also have experience with</p>\n<ul>\n<li>Real-time media protocols and stacks: WebRTC, RTP, gRPC bidirectional streaming, or WebSockets at scale</li>\n</ul>\n<ul>\n<li>Audio engineering fundamentals: codecs (Opus, AAC), voice activity detection, echo cancellation, jitter buffering, or audio DSP</li>\n</ul>\n<ul>\n<li>Low-latency ML inference serving, streaming model outputs, or GPU-based serving infrastructure</li>\n</ul>\n<ul>\n<li>Telephony, live streaming, video conferencing, or voice assistant platforms</li>\n</ul>\n<ul>\n<li>Mobile audio pipelines on iOS (AVAudioEngine, AudioUnits) or Android (Oboe, AAudio)</li>\n</ul>\n<ul>\n<li>Working alongside ML researchers to productionize models,speech experience is a plus but not required</li>\n</ul>\n<p>Representative projects</p>\n<ul>\n<li>Driving time-to-first-audio below human perceptual thresholds by co-designing the serving pipeline with the Audio research team</li>\n</ul>\n<ul>\n<li>Building a streaming inference orchestrator that interleaves speech recognition, LLM reasoning, and speech synthesis with overlapping execution</li>\n</ul>\n<ul>\n<li>Designing the voice mode API surface for the Anthropic API so developers can build their own voice agents on Claude</li>\n</ul>\n<ul>\n<li>Implementing graceful barge-in and interruption handling so users can cut Claude off mid-sentence naturally</li>\n</ul>\n<ul>\n<li>Instrumenting end-to-end audio quality metrics and building dashboards that catch regressions before users do</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_460d00aa-b48","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5172245008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000-$485,000 USD","x-skills-required":["Real-time media protocols and stacks","Audio engineering fundamentals","Low-latency ML inference serving","Distributed systems","Streaming pipelines","APIs"],"x-skills-preferred":["WebRTC","RTP","gRPC bidirectional streaming","WebSockets","Opus","AAC","Voice activity detection","Echo cancellation","Jitter buffering","Audio DSP","GPU-based serving infrastructure","Telephony","Live streaming","Video conferencing","Voice assistant platforms","Mobile audio pipelines on iOS","Android","Working alongside ML 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researchers","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6365e7d7-511"},"title":"Senior Forward Deployed Data Scientist/Engineer","description":"<p>We&#39;re hiring a Senior Forward Deployed Data Scientist / Engineer to work directly with customers on ambiguous, high-impact problems at the intersection of data science, product development, and AI deployment.</p>\n<p>This is not a traditional analytics role. On this team, data scientists do the core statistical and modeling work, but they also build real tools and products: evaluation explorers, operator workflows, decision-support systems, experimentation surfaces, and customer-specific AI/data applications that get used in production.</p>\n<p>The right candidate is strong in first-principles problem solving, rigorous measurement, and technical execution. They know how to define metrics, design experiments, diagnose failures, and build systems that people actually use. They are also comfortable using modern AI-assisted development tools to prototype and iterate quickly without sacrificing reliability, observability, or judgment. Python and SQL matter in this role, but as execution fluency in service of building better products and making better decisions.</p>\n<p>Responsibilities: Partner directly with enterprise customers to understand workflows, operational pain points, constraints, and success criteria Turn ambiguous business and product problems into measurable solutions with clear metrics, technical designs, and deployment plans Design and build internal and customer-facing data products, including evaluation tools, workflow applications, decision-support systems, and thin product layers on top of data/ML systems Build end-to-end solutions across data ingestion, transformation, experimentation, statistical modeling, deployment, monitoring, and iteration Design evaluation frameworks, benchmarks, and feedback loops for ML/LLM systems, human-in-the-loop workflows, and model-assisted operations Apply rigorous statistical thinking to experimentation, causal inference, metric design, forecasting, segmentation, diagnostics, and performance measurement Use AI-assisted development workflows to accelerate prototyping and product iteration, while maintaining strong engineering discipline Diagnose failure modes across data quality, model behavior, retrieval, workflow design, and user experience, and drive fixes into production Act as the voice of the customer to Product, Engineering, and Data Science, using field learnings to shape roadmap and platform capabilities</p>\n<p>Requirements: 5+ years of experience in data science, machine learning, quantitative engineering, or another highly analytical technical role Proven track record of shipping data, ML, or AI systems that delivered measurable business or product impact Exceptional ability to structure ambiguous problems, define the right success metrics, and translate them into executable technical plans Strong foundation in statistics, experimentation, causal reasoning, and measurement Experience building tools or products, not just analyses , for example internal workflow tools, evaluation systems, operator-facing products, experimentation platforms, or customer-specific applications Hands-on fluency in Python, SQL, and modern data/AI tooling; able to inspect data, prototype quickly, debug deeply, and productionize solutions that work Comfort using AI-assisted coding and development workflows to move from idea to usable product quickly Strong communication and stakeholder management skills; able to work effectively with customers, engineers, product teams, and executives High ownership and bias toward shipping in fast-moving environments with incomplete information</p>\n<p>Preferred qualifications: Experience in a forward deployed, solutions, consulting, or other client-facing technical role Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign</p>\n<p>What success looks like: Success in this role means taking a messy, high-stakes customer problem and turning it into a deployed system that is actually used. Sometimes that system is a model. Sometimes it is an evaluation framework. Sometimes it is an operator-facing tool or a lightweight data product that changes how decisions get made. In all cases, success is defined by measurable impact, rigorous evaluation, and reliable execution.</p>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.</p>\n<p>Salary Range: $167,200-$209,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_6365e7d7-511","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale AI","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4636227005","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$167,200-$209,000 USD","x-skills-required":["Python","SQL","Modern data/AI tooling","Statistics","Experimentation","Causal reasoning","Measurement","Data science","Machine learning","Quantitative engineering"],"x-skills-preferred":["Experience in a forward deployed, solutions, consulting, or other client-facing technical role","Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products","Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow","Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery","Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems","Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling","Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign"],"datePosted":"2026-04-18T15:59:44.618Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, SQL, Modern data/AI tooling, Statistics, Experimentation, Causal reasoning, Measurement, Data science, Machine learning, Quantitative engineering, Experience in a forward deployed, solutions, consulting, or other client-facing technical role, Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products, Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow, Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery, Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems, Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling, Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":167200,"maxValue":209000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_539e2a23-ddf"},"title":"Tech Lead Manager- MLRE, ML Systems","description":"<p>You will lead the development of our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline.</p>\n<p>You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimising the platform to enable our next generation LLM training, inference and data curation.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Building, profiling and optimising our training and inference framework.</li>\n<li>Collaborating with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.</li>\n<li>Researching and integrating state-of-the-art technologies to optimise our ML system.</li>\n</ul>\n<p>The ideal candidate will have:</p>\n<ul>\n<li>Passionate about system optimisation.</li>\n<li>Experience with multi-node LLM training and inference.</li>\n<li>Experience with developing large-scale distributed ML systems.</li>\n<li>Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.</li>\n<li>Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash attention, etc.</li>\n</ul>\n<p>Nice to haves include demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.</p>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_539e2a23-ddf","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4618046005","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$264,800-$331,000 USD","x-skills-required":["system optimisation","multi-node LLM training and inference","large-scale distributed ML systems","post-training methods","software engineering skills","CUDA","PyTorch","transformers","flash attention"],"x-skills-preferred":["next generation use cases for large language models","instruction tuning","RLHF","tool use","reasoning","agents","multimodal"],"datePosted":"2026-04-18T15:59:21.558Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"system optimisation, multi-node LLM training and inference, large-scale distributed ML systems, post-training methods, software engineering skills, CUDA, PyTorch, transformers, flash attention, next generation use cases for large language models, instruction tuning, RLHF, tool use, reasoning, agents, multimodal","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":264800,"maxValue":331000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_150ca1e8-f29"},"title":"Research Economist, Economic Research","description":"<p>As a Research Economist at Anthropic, you will work to measure and understand AI&#39;s effects on the global economy. You will make fundamental contributions to the development of the Anthropic Economic Index, establishing new methodologies to measure the usage, diffusion, and impact of AI throughout the economy using privacy-preserving tools and novel data sources. You will use frontier methods in econometrics, machine learning, and structural estimation. Such rigour will drive impact, shaping both policy discussions externally and informing Anthropic’s internal business and product decisions.</p>\n<p>Our team combines rigorous empirical methods with novel measurement approaches. We&#39;re building first-of-its-kind datasets tracking AI&#39;s impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system (Clio), we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Make fundamental contributions to the development and expansion of the Anthropic Economic Index, including quarterly reports and industry-specific deep dives</li>\n</ul>\n<ul>\n<li>Design and conduct empirical research on AI&#39;s economic effects, drawing on external data sources and the privacy-preserving measurement systems internally</li>\n</ul>\n<ul>\n<li>Develop new methodological approaches for studying AI&#39;s impact on:</li>\n</ul>\n<ul>\n<li>Labor markets and the future of work</li>\n</ul>\n<ul>\n<li>Productivity and task transformation</li>\n</ul>\n<ul>\n<li>Economic inequality and displacement</li>\n</ul>\n<ul>\n<li>Industry-specific disruption and adaptation</li>\n</ul>\n<ul>\n<li>Aggregate economic trajectories (GDP, productivity, unemployment) under varying AI-adoption scenarios</li>\n</ul>\n<ul>\n<li>Develop causal-inference tooling , e.g. surrogate indexes, heterogeneous-effect pipelines , to help Anthropic evaluate the downstream economic consequences of its own compute, product, and pricing decisions</li>\n</ul>\n<ul>\n<li>Build and maintain relationships with academic institutions, policy think tanks, and other research partners</li>\n</ul>\n<ul>\n<li>Work cross-functionally with other technical teams to improve our measurement infrastructure and data collection</li>\n</ul>\n<ul>\n<li>Translate research insights into actionable recommendations for both product decisions and policy discussions</li>\n</ul>\n<ul>\n<li>Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders</li>\n</ul>\n<p>You May Be a Good Fit If You Have:</p>\n<ul>\n<li>PhD in Economics</li>\n</ul>\n<ul>\n<li>Strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning</li>\n</ul>\n<ul>\n<li>Experience relevant to the study of AI’s impact on the economy, including:</li>\n</ul>\n<ul>\n<li>Labor market analysis and occupational change</li>\n</ul>\n<ul>\n<li>Task-based approaches to technological transformation</li>\n</ul>\n<ul>\n<li>Large-scale data analysis and econometric methods</li>\n</ul>\n<ul>\n<li>Large language models for social science research</li>\n</ul>\n<ul>\n<li>Policy-relevant economic research</li>\n</ul>\n<ul>\n<li>Experimental and quasi-experimental methods for causal inference</li>\n</ul>\n<ul>\n<li>Macroeconomic modeling and time series forecasting</li>\n</ul>\n<ul>\n<li>Agent-based modeling or large-scale simulation</li>\n</ul>\n<ul>\n<li>Technical skills including:</li>\n</ul>\n<ul>\n<li>Proficiency in Python, R, SQL, or similar tools for large-scale data analysis</li>\n</ul>\n<ul>\n<li>Experience working with 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We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.</p>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP)</li>\n<li>Python or Rust</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p>Representative projects across the org:</p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p>Annual compensation range for this role is €235,000-€295,000 EUR.</p>\n<p>Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience</p>\n<p>Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience</p>\n<p>Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position</p>\n<p>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.</p>\n<p>How we&#39;re different:</p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p>The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>\n<p>Come work with us!</p>\n<p>Anthropic is a public benefit corporation headquartered in San Francisco. 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Our platform powers cutting-edge research and production systems, supporting both internal and external use cases across various environments.</p>\n<p>The ideal candidate combines strong ML fundamentals with deep expertise in backend system design. 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The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You&#39;ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.</p>\n<p>Please note that our policy requires a 90-day waiting period before reconsidering candidates for the same role. 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You will collaborate closely with product teams to push model frontiers and deliver exceptional end-to-end user experiences.</p>\n<p>Key responsibilities include creating and driving engineering agendas to advance multimodal capabilities, improving data quality through annotation, filtering, augmentation, synthetic generation, captioning, and in-depth data studies, designing evaluation frameworks, metrics, benchmarks, evals, and reward models tailored to image/video/audio quality and coherence, implementing efficient algorithms for state-of-the-art model performance, and developing scalable data collection and processing pipelines for multimodal (primarily image/video-focused) datasets.</p>\n<p>The ideal candidate will have a track record in leading studies that significantly improve neural network capabilities and performance through better data or modeling, experience in data-driven experiment designs, systematic analysis, and iterative model debugging, experience developing or working with large-scale distributed machine learning systems, and ability to deliver optimal end-to-end user experiences.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_8a3caae4-044","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5051985007","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$180,000 - $440,000 USD","x-skills-required":["data curation","modeling","training","inference serving","product integration","large-scale distributed machine learning systems"],"x-skills-preferred":["SFT","RL","evals","human/synthetic data collection","agentic systems","Python","JAX/XLA","PyTorch","Rust/C++","Spark","Ray"],"datePosted":"2026-04-18T15:58:43.641Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto, CA; Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"data curation, modeling, training, inference serving, product integration, large-scale distributed machine learning systems, SFT, RL, evals, human/synthetic data collection, agentic systems, Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":440000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6683dad6-0d9"},"title":"Senior Data Scientist","description":"<p>Join us to build the decision engine for better mental health outcomes.</p>\n<p>As a Senior Data Scientist, you will sit in the heart of a cross-functional product team and help turn messy, real-world signals into clear decisions. You will make sure we are capturing the right data, designing experiments that tell us what is actually driving outcomes, and translating findings into recommendations that teams can act on quickly.</p>\n<p>When the insight is stable and valuable, you will help operationalize it through predictive models that improve provider and patient experiences.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Being the analytics partner inside the pod, working closely with Product, Engineering, Design, Ops, and Clinical stakeholders to define questions, metrics, guardrails, and decision rules.</li>\n<li>Running rigorous experiments, designing and analyzing A/B tests and quasi-experiments with clear hypotheses, power considerations, and pre-defined success criteria.</li>\n<li>Connecting behavior to strategy, using funnel, cohort, segmentation, and lifecycle analysis to understand how people and providers experience Headway, and where product changes will have the biggest impact.</li>\n<li>Using causal inference when experiments are not possible, applying approaches like diff-in-diff, matching, and regression-based designs with principled uncertainty quantification.</li>\n<li>Building models when they should exist, developing predictive models that operationalize vetted insights (feature development, validation, backtesting, calibration), with clear launch criteria and monitoring plans.</li>\n<li>Creating decision-ready work, producing analysis and narratives that are crisp, honest about uncertainty, and drive action.</li>\n</ul>\n<p>To be successful in this role, you will need:</p>\n<ul>\n<li>6+ years using data to drive product or business decisions in product, growth, engineering, or operations environments.</li>\n<li>Strong SQL and strong proficiency in Python or R for analysis and modeling.</li>\n<li>Demonstrated depth in experimentation and causal inference under real-world constraints.</li>\n<li>Practical modeling skill: feature engineering, model comparison, cross-validation or backtesting, calibration, and post-launch monitoring.</li>\n<li>Strong product sense and opinions, including a track record of connecting analytics recommendations to measurable outcomes.</li>\n<li>Clear communication: you can explain complex work to non-technical audiences without losing the truth.</li>\n<li>A self-starter mindset: you prioritize well, follow through, and do not need heavy oversight.</li>\n<li>Motivation for our mission: improving access and affordability in mental healthcare.</li>\n</ul>\n<p>The expected base pay range for this position is $180,000 - $225,000, based on a variety of factors including qualifications, experience, and geographic location. 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If there’s something important to you that’s not on this list, talk to us!</p>\n<p>Competitive salary, annual bonus and equity</p>\n<p>Regular compensation reviews - we reward great work!</p>\n<p>Unlimited access to Claude Code and best-in-class AI tools; experimentation &amp; building is encouraged &amp; celebrated.</p>\n<p>Generous paid time off above statutory minimum</p>\n<p>Hybrid working</p>\n<p>MacBooks are our standard, but we also offer Windows for certain roles when needed.</p>\n<p>Fun events for employees, friends, and family!</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a 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easy for teams to say “we do not know yet” without losing momentum, and easy for leaders to understand what is safe to act on.</li>\n<li>Mentor and set the bar.</li>\n<li>Coach other data scientists and analytics leaders.</li>\n<li>Create review standards for causal work,</li>\n<li>Support hiring for methodological depth,</li>\n<li>Represent Headway’s measurement philosophy internally and externally when appropriate.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>12+ years of experience applying causal inference, experimentation, and advanced statistics to real-world product, growth, or operational decisions (or equivalent depth demonstrated through scope and outcomes).</li>\n<li>Deep expertise in causal inference across randomized and observational settings, including practical strategy for when clean experiments are not possible.</li>\n<li>Deep expertise in Bayesian methods for experimentation and decision-making, and strong judgment about when Bayesian approaches outperform frequentist defaults and when they do not.</li>\n<li>Strong SQL and strong proficiency in Python or R, including building reusable analysis tools and improving team workflows.</li>\n<li>Track record of setting org-wide standards that materially improved decision quality and execution velocity.</li>\n<li>Executive-level communication and influence: you can drive alignment across Product, Growth, Ops, Finance, and Engineering.</li>\n<li>Comfort operating in ambiguity, and the ability to turn it into crisp frameworks, clear recommendations, and measurable outcomes.</li>\n<li>Motivation for our mission: improving access and affordability in mental healthcare.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience in marketplaces, healthcare, insurance, or other regulated and complex incentive systems.</li>\n<li>Experience with experimentation under interference and network effects.</li>\n<li>Experience building experimentation platforms, analysis libraries, or statistical tooling used 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Focus on models that survive contact with reality: calibration, backtesting, and decision usefulness.</li>\n</ul>\n<ul>\n<li>Partner with Engineering on the measurement plumbing. Improve event instrumentation, identity resolution assumptions, offline conversion integration, and data quality monitoring so measurement is robust. Advocate for minimal, decision-critical requirements that unlock reliable learning.</li>\n</ul>\n<ul>\n<li>Design learning loops that scale. Create repeatable experimentation and analysis templates for channel and creative testing, including measurement of message by audience by surface. Increase testing velocity without lowering the truth standard.</li>\n</ul>\n<ul>\n<li>Influence strategy, not just reporting. Bring an evidence-based point of view on channel allocation, growth constraints, saturation, diminishing returns, and the tradeoffs between short-term acquisition and long-term retention and care outcomes.</li>\n</ul>\n<ul>\n<li>Uplevel the team. Mentor analysts and data scientists working on growth, set quality standards, and help establish best practices across experimentation, causal inference, and forecasting.</li>\n</ul>\n<p>What will make you successful:</p>\n<ul>\n<li>10+ years using data science, analytics, and experimentation to drive decisions in marketing, growth, or marketplace environments (or equivalent scope and demonstrated impact).</li>\n</ul>\n<ul>\n<li>Deep expertise in causal inference and incrementality in real-world marketing systems: you know the failure modes (selection bias, channel cannibalization, platform noise, attribution myths) and how to design around them.</li>\n</ul>\n<ul>\n<li>Strong SQL plus strong proficiency in Python or R, with the ability to build reliable, reusable analytical workflows.</li>\n</ul>\n<ul>\n<li>Practical modeling skill, especially as applied to marketing and growth: cohorting, forecasting, LTV estimation, saturation and diminishing returns, MMM concepts, calibration and monitoring.</li>\n</ul>\n<ul>\n<li>Track record of influencing executive decisions with clear recommendations and measurable outcomes, not just analysis.</li>\n</ul>\n<ul>\n<li>Excellent communication: you can make complex measurement logic understandable and defensible to non-technical partners, and you can call out uncertainty without losing momentum.</li>\n</ul>\n<ul>\n<li>High ownership and strong judgment: you prioritize what changes decisions, you move quickly, and you know when to slow down because the risk is real.</li>\n</ul>\n<ul>\n<li>You are motivated by the mission. Access and affordability in mental healthcare are not abstract problems here.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience with geo experiments, marketplace constraints, or capacity-aware marketing optimization.</li>\n</ul>\n<ul>\n<li>Experience measuring acquisition quality beyond conversion: downstream engagement, retention, clinical matching quality, and unit economics.</li>\n</ul>\n<ul>\n<li>Familiarity with lifecycle marketing measurement (incrementality, uplift, experimentation design for messaging).</li>\n</ul>\n<ul>\n<li>Experience partnering with Finance on budget allocation, payback, and scenario planning.</li>\n</ul>\n<ul>\n<li>Comfort working with imperfect identity, privacy constraints, and evolving attribution ecosystems.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ca38c08d-e8f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Headway","sameAs":"https://www.headway.com/","logo":"https://logos.yubhub.co/headway.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/headway/jobs/5751646004","x-work-arrangement":"remote","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$212,000 - $265,000","x-skills-required":["data science","analytics","experimentation","marketing","growth","SQL","Python","R","causal inference","incrementality","modeling","forecasting","LTV estimation","saturation","diminishing returns","MMM concepts","calibration","monitoring"],"x-skills-preferred":["geo experiments","marketplace constraints","capacity-aware marketing optimization","acquisition quality","downstream engagement","retention","clinical matching quality","unit economics","lifecycle marketing measurement","uplift","experimentation design for messaging","budget allocation","payback","scenario planning"],"datePosted":"2026-04-18T15:57:07.522Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Marketing","industry":"Healthcare","skills":"data science, analytics, experimentation, marketing, growth, SQL, Python, R, causal inference, incrementality, modeling, forecasting, LTV estimation, saturation, diminishing returns, MMM concepts, calibration, monitoring, geo experiments, marketplace constraints, capacity-aware marketing optimization, acquisition quality, downstream engagement, retention, clinical matching quality, unit economics, lifecycle marketing measurement, uplift, experimentation design for messaging, budget allocation, payback, scenario planning","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":212000,"maxValue":265000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c38cbb6f-4b7"},"title":"Staff Software Engineer, Inference","description":"<p>Job Title: Staff Software Engineer, Inference\\n\\nLocation: Dublin, IE\\n\\nDepartment: Software Engineering - Infrastructure\\n\\nJob Description:\\n\\nAbout Anthropic\\n\\nAnthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.\\n\\nAbout the role:\\n\\nOur Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.\\n\\nThe team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.\\n\\nAs a Staff Software Engineer on our Inference team, you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.\\n\\nStrong candidates may also have experience with:\\n\\n- High-performance, large-scale distributed systems\\n\\n- Implementing and deploying machine learning systems at scale\\n\\n- Load balancing, request routing, or traffic management systems\\n\\n- LLM inference optimization, batching, and caching strategies\\n\\n- Kubernetes and cloud infrastructure (AWS, GCP)\\n\\n- Python or Rust\\n\\nYou may be a good fit if you:\\n\\n- Have significant software engineering experience, particularly with distributed systems\\n\\n- Are results-oriented, with a bias towards flexibility and impact\\n\\n- Pick up slack, even if it goes outside your job description\\n\\n- Want to learn more about machine learning systems and infrastructure\\n\\n- Thrive in environments where technical excellence directly drives both business results and research breakthroughs\\n\\n- Care about the societal impacts of your work\\n\\nRepresentative projects across the org:\\n\\n- Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators\\n\\n- Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads\\n\\n- Building production-grade deployment pipelines for releasing new models to millions of users\\n\\n- Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage\\n\\n- Contributing to new inference features (e.g., structured sampling, prompt caching)\\n\\n- Supporting inference for new model architectures\\n\\n- Analyzing observability data to tune performance based on real-world production workloads\\n\\n- Managing multi-region deployments and geographic routing for global customers\\n\\nDeadline to apply: None. Applications will be reviewed on a rolling basis.\\n\\nThe annual compensation range for this role is listed below.\\n\\nFor sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\n\\nAnnual Salary:€295.000-€355.000 EUR\\n\\nLogistics\\n\\nMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\\n\\nRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience\\n\\nMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\\n\\nLocation-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\\n\\nVisa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\\n\\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\\n\\nYour safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.\\n\\nHow we&#39;re different\\n\\nWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.\\n\\nThe easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.\\n\\nCome work with us!\\n\\nAnthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates&#39; AI Usage: Learn about our policy for using AI in our application process</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c38cbb6f-4b7","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5150472008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"€295.000-€355.000 EUR","x-skills-required":["performance optimization","distributed systems","large-scale service orchestration","intelligent request routing","LLM inference optimization","batching strategies","multi-accelerator deployments","Kubernetes","cloud infrastructure","Python","Rust"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:57:00.340Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dublin, IE"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"performance optimization, distributed systems, large-scale service orchestration, intelligent request routing, LLM inference optimization, batching strategies, multi-accelerator deployments, Kubernetes, cloud infrastructure, Python, Rust"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ac45e205-e7d"},"title":"Engineering Manager, Inference Routing and Performance","description":"<p><strong>About the role\\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what&#39;s already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\\n\\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn&#39;t have been shed.\\n\\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic&#39;s inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\\n\\nAs Anthropic moves from &quot;many independent inference replicas&quot; toward &quot;a single warehouse-scale computer running a coordinated program,&quot; Dystro is the coordination layer. This is a deeply technical team.\\n\\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\\n\\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\\n\\nThe EM for this team doesn&#39;t need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\\n\\nYou&#39;ll inherit a strong team of distributed-systems engineers, and you&#39;ll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\\n\\nThe job is holding both.\\n\\n## Representative work:\\nThings the Inference Routing EM actually spends time on:\\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they&#39;d be additive to a team that already goes deep\\n\\n## What you&#39;ll do:\\nDrive system-level performance\\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\\n- Build the team&#39;s habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\\n\\nDeliver reliably and operate cleanly\\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\\n- Run the team&#39;s operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You&#39;ll make sure commitments are realistic, dependencies are understood, and nobody is surprised\\n\\nBuild and grow the team\\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you&#39;ll help make that collaboration pattern productive\\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\\n\\n## You may be a good fit if you:\\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\\n- Are curious about machine learning systems. You don&#39;t need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\\n\\nStrong candidates may also have:\\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\\n- Led teams at supercomputing or hyperscaler infrastructure scale\\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\\n\\nThe annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\nAnnual Salary: $405,000-$485,000 USD</strong></p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ac45e205-e7d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5155391008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$405,000-$485,000 USD","x-skills-required":["engineering management","distributed systems","load balancing","scheduling","cache-coherent distributed state","high-performance networking","machine learning systems"],"x-skills-preferred":["LLM inference serving","cluster schedulers","load balancers","service meshes","coordination planes","heterogeneous accelerator fleets","GPU/TPU/Trainium","GPU/accelerator programming","ML framework internals","OS-level performance debugging"],"datePosted":"2026-04-18T15:56:48.587Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"engineering management, distributed systems, load balancing, scheduling, cache-coherent distributed state, high-performance networking, machine learning systems, LLM inference serving, cluster schedulers, load balancers, service meshes, coordination planes, heterogeneous accelerator fleets, GPU/TPU/Trainium, GPU/accelerator programming, ML framework internals, OS-level performance debugging","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":405000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c81cbaa1-56a"},"title":"Engineering Technical Program Manager - W&B Platform","description":"<p>The Weights &amp; Biases (W&amp;B) team builds the developer platform trusted by machine learning practitioners to track, manage, and scale their ML workflows. As a Technical Program Manager focused on platform reliability and release management, you&#39;ll be at the centre of our platform&#39;s growth and stability.</p>\n<p>You will partner with engineering teams within W&amp;B and CoreWeave AI/ML Platform Services (AMPS) to ensure W&amp;B integrates seamlessly into the broader ML ecosystem, while maintaining high reliability and predictable releases.</p>\n<p>This role is ideal for someone who thrives in cross-functional environments, has a strong grasp of developer workflows, and excels at creating repeatable, reliable program structures that scale.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Drive end-to-end program management for critical platform initiatives.</li>\n<li>Build and run release management processes, ensuring predictable and high-quality delivery cycles.</li>\n<li>Partner with engineering and product to define success metrics, manage risks, and ensure on-time delivery.</li>\n<li>Build and scale incident management and RCA processes for W&amp;B services.</li>\n<li>Improve the predictability and visibility of releases across teams, introducing dashboards, retrospectives, and program forums.</li>\n<li>Collaborate with TPMs and engineering leaders across W&amp;B and CoreWeave to ensure end-to-end reliability across the ML developer stack.</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<ul>\n<li>Bachelor&#39;s degree in a technical field or equivalent experience.</li>\n<li>5+ years of program management experience in SaaS, developer tools, or ML/AI platforms.</li>\n<li>Proven experience running release management programs and incident management processes.</li>\n<li>Strong technical fluency in cloud computing, developer workflows, and CI/CD practices.</li>\n<li>Excellent communication and facilitation skills with diverse technical and non-technical audiences.</li>\n<li>Track record of improving reliability, efficiency, and predictability in software delivery.</li>\n</ul>\n<p><strong>Additional Qualifications</strong></p>\n<ul>\n<li>Familiarity with ML workflows, model training/inference, and developer productivity tools.</li>\n<li>Experience building integrations between SaaS platforms, APIs, and cloud services.</li>\n<li>Strong background in reliability engineering practices and DevOps program leadership.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c81cbaa1-56a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"CoreWeave","sameAs":"https://www.coreweave.com","logo":"https://logos.yubhub.co/coreweave.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/coreweave/jobs/4610109006","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$177,000 to $237,000","x-skills-required":["cloud computing","developer workflows","CI/CD practices","program management","release management","incident management","reliability engineering"],"x-skills-preferred":["ML workflows","model training/inference","developer productivity tools","integration between SaaS platforms, APIs, and cloud services"],"datePosted":"2026-04-18T15:56:43.785Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"cloud computing, developer workflows, CI/CD practices, program management, release management, incident management, reliability engineering, ML workflows, model training/inference, developer productivity tools, integration between SaaS platforms, APIs, and cloud services","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":177000,"maxValue":237000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d799d883-0dd"},"title":"Solutions Architect- Networking","description":"<p>As a Solutions Architect at CoreWeave, you will play a vital role in leading innovation at every turn. You will have the opportunity to demonstrate thought leadership and engage hands-on throughout our customers&#39; entire lifecycle. From establishing their Kubernetes environment to developing proofs of concept, onboarding, and optimizing workloads, you will lead innovation at every turn.</p>\n<p>In this role, you will:</p>\n<p>Serve as the primary technical point of contact for customers, establishing strong technical relationships and ensuring their success with CoreWeave&#39;s cloud infrastructure offerings, focusing on networking technologies within high-performance compute (HPC) environments Collaborate closely with customers to understand their unique business needs and create, prototype, and deploy tailored solutions that align with their requirements. Lead proof of concept initiatives to showcase the value and viability of CoreWeave&#39;s solutions within specific environments. Drive technical leadership and direction during customer meetings, presentations, and workshops, addressing any technical queries or concerns that arise. Act as a virtual member of CoreWeave&#39;s Networking product and engineering teams, identifying opportunities for product enhancement and collaborating with engineers to implement your suggestions. Offer valuable insights on product features, functionality, and performance, contributing regularly to discussions about product strategy and architecture. Conduct periodic technical reviews and assessments of customer workloads, pinpointing opportunities for workload optimization and suggesting suitable solutions. Stay informed of the latest developments and trends in Kubernetes, cloud computing and infrastructure, sharing your thought leadership with customers and internal stakeholders. Lead the prototyping and initiation of research and development efforts for emerging products and solutions, delivering prototypes and key insights for internal consumption. Represent CoreWeave at conferences and industry events, with occasional travel as required.</p>\n<p>Who You Are:</p>\n<p>B.S. in Computer Science or a related technical discipline, or equivalent experience 7+ years of proven experience as a Solutions Architect, engineer, researcher, or technical account manager in cloud infrastructure focusing on building distributed systems or HPC/cloud services, with an expertise focused on infrastructure networking. Fluency in cloud computing concepts, architecture, and technologies with hands-on experience in designing and implementing cloud solutions Proven track record with building customer relationships, communicating clearly and the ability to break down complex technical concepts to both technical and non-technical audiences Expertise with a broad range of networking technologies and topics, with a familiarity to understand the needs and use cases is it relates to securing and enabling high performance networking environments. Experience with managing infrastructure networking, Kubernnetes CSI management, and private networking concepts Familiar with NVIDIA GPUs typically used in AI/ML applications and associated technologies such as Infiniband and NVIDIA Collective Communications Library (NCCL)</p>\n<p>Preferred:</p>\n<p>Code contributions to open-source inference frameworks Experience with scripting and automation related to network technologies Experience with building solutions across multi-cloud environments Client or customer-facing publications/talks on latency, optimization, or advanced model-server architectures</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d799d883-0dd","directApply":true,"hiringOrganization":{"@type":"Organization","name":"CoreWeave","sameAs":"https://www.coreweave.com","logo":"https://logos.yubhub.co/coreweave.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/coreweave/jobs/4568528006","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$165,000 to $220,000","x-skills-required":["cloud computing","Kubernetes","infrastructure networking","high-performance computing","networking technologies","NVIDIA GPUs","Infiniband","NVIDIA Collective Communications Library (NCCL)"],"x-skills-preferred":["open-source inference frameworks","scripting and automation","multi-cloud environments","latency, optimization, or advanced model-server architectures"],"datePosted":"2026-04-18T15:56:27.053Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Livingston, NJ / New York, NY / Sunnyvale, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"cloud computing, Kubernetes, infrastructure networking, high-performance computing, networking technologies, NVIDIA GPUs, Infiniband, NVIDIA Collective Communications Library (NCCL), open-source inference frameworks, scripting and automation, multi-cloud environments, latency, optimization, or advanced model-server architectures","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":165000,"maxValue":220000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_32c0c69a-037"},"title":"Staff Software Engineer, Inference","description":"<p><strong>About the role:</strong></p>\n<p>Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>As a Staff Software Engineer on our Inference team, you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. 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This is foundational work for how millions of people will interact with AI.</p>\n<p>The Voice Platform team designs and operates the serving systems, streaming pipelines, and APIs that bring Anthropic&#39;s audio models from research into production across Claude.ai, our mobile apps, and the Anthropic API. You&#39;ll work at the intersection of real-time media, low-latency inference, and distributed systems,building infrastructure where every millisecond of latency is felt by the user.</p>\n<p>We partner closely with the Audio research team, who train the speech understanding and generation models, and with product teams shipping voice experiences to users. 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You&#39;ll drive strategic initiatives across inference runtime and accelerator performance,coordinating model launches, managing cross-platform dependencies, and ensuring reliability across multiple hardware targets.</p>\n<p>This role is essential for keeping our most contended infrastructure teams shipping effectively while Research, Product, and Safety all depend on their output.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Systems Integration &amp; Coordination: Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels between teams. Drive end-to-end planning for major infrastructure transitions including platform modernization and new tech adoption.</li>\n</ul>\n<ul>\n<li>Performance &amp; Efficiency: Partner with engineering teams to identify optimisation opportunities, track performance metrics, and prioritise work that unlocks capacity gains. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability.</li>\n</ul>\n<ul>\n<li>Launch Coordination: Drive end-to-end readiness for model and feature launches across multiple hardware platforms. Establish processes for cross-platform validation, manage launch timelines, and ensure smooth handoffs between runtime, accelerator, and downstream teams.</li>\n</ul>\n<ul>\n<li>Strategic Planning: Own and prioritise the inference deployment roadmap, working closely with engineering leadership to prioritise initiatives and manage dependencies. Provide visibility into upcoming changes and their organisational impact.</li>\n</ul>\n<ul>\n<li>Stakeholder Communication: Build strong relationships across research, engineering, and product teams to understand requirements and constraints. Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines.</li>\n</ul>\n<ul>\n<li>Process Improvement: Identify inefficiencies in current workflows and drive systematic improvements. Establish metrics and dashboards to track infrastructure health, capacity utilisation, and deployment success rates.</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems</li>\n</ul>\n<ul>\n<li>Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks.</li>\n</ul>\n<ul>\n<li>Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives</li>\n</ul>\n<ul>\n<li>Have strong stakeholder management skills and can build trust with both technical and non-technical partners</li>\n</ul>\n<ul>\n<li>Are comfortable navigating competing priorities and using data to drive technical decisions</li>\n</ul>\n<ul>\n<li>Have experience with infrastructure scaling initiatives, hardware integrations, or deployment governance</li>\n</ul>\n<ul>\n<li>Thrive in fast-paced environments and can balance strategic planning with tactical execution</li>\n</ul>\n<ul>\n<li>Are passionate about AI infrastructure and understand the unique challenges of deploying and scaling large language models</li>\n</ul>\n<p>Deadline to apply: None, applications will be received on a rolling basis.</p>\n<p>The annual compensation range for this role is $290,000-$365,000 USD.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a 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Prioritisation Initiatives, Dependencies, Upcoming Changes, Organisational Impact, Stakeholder Communication, Requirements and Constraints, Technical Complexities, Leadership Updates, Priorities and Timelines, Process Improvement, Metrics and Dashboards, Infrastructure Health, Capacity Utilisation, Deployment Success Rates","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":290000,"maxValue":365000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4aaad5cf-9d0"},"title":"Technical Program Manager, Platform","description":"<p>As a Technical Program Manager for Platform, you&#39;ll own the programs that stand up and operate Anthropic&#39;s APIs and serving infrastructure across multiple cloud environments.</p>\n<p>This means driving deployments from scoping through production, running the platform work that spans them, and working across API, Platform Foundations, Security, our cloud provider counterparts, and whoever else is on the critical path when dependencies and tradeoffs pile up.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own end-to-end program execution for Anthropic’s API across major cloud deployments, from scoping through production launch and steady-state operations</li>\n</ul>\n<ul>\n<li>Drive the platform programs that cut across individual deployments: the shared foundations that get built once and reused, not rebuilt per cloud</li>\n</ul>\n<ul>\n<li>Act as a primary coordination point with cloud provider counterparts, keeping engagement clean across multiple internal teams with touchpoints into the same partner</li>\n</ul>\n<ul>\n<li>Partner with engineering leadership to turn technical direction into executable plans with clear owners, dependencies, and risk tracking</li>\n</ul>\n<ul>\n<li>Build the program scaffolding (roadmaps, status reporting, decision logs, escalation paths) that lets a fast-moving org 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depended on, and delivered anyway</li>\n</ul>\n<ul>\n<li>Have direct experience with multi-cloud or hybrid cloud environments, large-scale migrations, or building platform abstraction layers</li>\n</ul>\n<ul>\n<li>Have worked with major cloud providers (AWS, GCP, Azure) or similar large technology partners, and know how to keep those relationships productive when priorities diverge</li>\n</ul>\n<ul>\n<li>Are comfortable operating in ambiguity on the long arc while being ruthlessly concrete on what ships this quarter and who owns it</li>\n</ul>\n<ul>\n<li>Have a track record of making a program get cheaper to run the second and third time, not just landing the first instance</li>\n</ul>\n<ul>\n<li>Thrive in environments where the plan you wrote last month needs rewriting, without losing the thread on what matters</li>\n</ul>\n<p>Strong candidates may also:</p>\n<ul>\n<li>Have experience with production serving infrastructure, inference systems, or ML platform work</li>\n</ul>\n<ul>\n<li>Have moved between senior IC and management roles, or have interest in doing so</li>\n</ul>\n<ul>\n<li>Have worked at a company rebuilding systems and org in flight during rapid scale-up</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4aaad5cf-9d0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5157003008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$365,000-$435,000 USD","x-skills-required":["Cloud APIs","Infrastructure","Distributed Systems","Platform Engineering","Cloud Provider Partnerships","Program Management","Technical Leadership"],"x-skills-preferred":["Production Serving 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a software engineer for GenAI inference, you will help design, develop, and optimize the inference engine that powers Databricks&#39; Foundation Model API. You&#39;ll work at the intersection of research and production, ensuring our large language model (LLM) serving systems are fast, scalable, and efficient.</p>\n<p>Your work will touch the full GenAI inference stack , from kernels and runtimes to orchestration and memory management. You will contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Collaborating with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine</li>\n<li>Optimizing for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators</li>\n<li>Building and maintaining instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations</li>\n<li>Developing and enhancing scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads</li>\n<li>Supporting reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning</li>\n<li>Integrating with federated, distributed inference infrastructure – orchestrate across nodes, balance load, handle communication overhead</li>\n<li>Collaborating cross-functionally: with platform engineers, cloud infrastructure, and security/compliance teams</li>\n<li>Documenting and sharing learnings, contributing to internal best practices and open-source efforts when possible</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>BS/MS/PhD in Computer Science, or a related field</li>\n<li>Strong software engineering background (3+ years or equivalent) in performance-critical systems</li>\n<li>Solid understanding of ML inference internals: attention, MLPs, recurrent modules, quantization, sparse operations, etc.</li>\n<li>Hands-on experience with CUDA, GPU programming, and key libraries (cuBLAS, cuDNN, NCCL, etc.)</li>\n<li>Comfortable designing and operating distributed systems, including RPC frameworks, queuing, RPC batching, sharding, memory partitioning</li>\n<li>Demonstrated ability to uncover and solve performance bottlenecks across layers (kernel, memory, networking, scheduler)</li>\n<li>Experience building instrumentation, tracing, and profiling tools for ML models</li>\n<li>Ability to work closely with ML researchers, translate novel model ideas into production systems</li>\n<li>Ownership mindset and eagerness to dive deep into complex system challenges</li>\n<li>Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f2196e99-854","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8202670002","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$142,200-$204,600 USD","x-skills-required":["software engineering","performance-critical systems","ML inference internals","CUDA","GPU programming","distributed systems","RPC frameworks","queuing","RPC batching","sharding","memory partitioning","instrumentation","tracing","profiling tools","ML researchers","complex system challenges"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:54:17.777Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, performance-critical systems, ML inference internals, CUDA, GPU programming, distributed systems, RPC frameworks, queuing, RPC batching, sharding, memory partitioning, instrumentation, tracing, profiling tools, ML researchers, complex system challenges","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":142200,"maxValue":204600,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_96d05ee1-799"},"title":"Staff Software Engineer, Cluster Orchestration","description":"<p><strong>Job Description</strong></p>\n<p>CoreWeave is The Essential Cloud for AI. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence.</p>\n<p>Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability.</p>\n<p>Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025.</p>\n<p><strong>About the Role</strong></p>\n<p>As part of the Cluster Orchestration team, you will play a key role in advancing CoreWeave&#39;s orchestration platform including SUNK (Slurm on Kubernetes) and beyond, our Kubernetes-native foundation that powers AI training and inference at scale.</p>\n<p>This is an opportunity to help shape one of the most critical layers of the AI cloud: ensuring workloads run seamlessly, reliably, and efficiently across massive GPU clusters.</p>\n<p>By building the systems that eliminate infrastructure bottlenecks and create new orchestration capabilities, you will directly empower customers to innovate faster and push the boundaries of what&#39;s possible with AI.</p>\n<p><strong>What You&#39;ll Do</strong></p>\n<p>As a Staff Engineer, you will be a technical leader shaping the long-term strategy for CoreWeave&#39;s orchestration platform.</p>\n<p>You&#39;ll define architectural direction, own critical parts of the orchestration platform and other managed services, and drive cross-org initiatives in scheduling, quota enforcement, and scaling at hyperscale.</p>\n<p>You&#39;ll mentor senior engineers, establish org-wide best practices in reliability and observability, and ensure CoreWeave&#39;s orchestration layer evolves to meet the demands of next-generation AI workloads.</p>\n<p><strong>Who You Are</strong></p>\n<ul>\n<li>8+ years of software engineering experience.</li>\n</ul>\n<ul>\n<li>Proven track record designing and operating large-scale distributed systems in production.</li>\n</ul>\n<ul>\n<li>Deep expertise in Slurm/Kubernetes internals and cloud-native development.</li>\n</ul>\n<ul>\n<li>Advanced proficiency in Go and distributed systems design and cloud-native development.</li>\n</ul>\n<ul>\n<li>Experience setting technical direction and influencing cross-team architecture.</li>\n</ul>\n<ul>\n<li>Bachelor&#39;s or Master&#39;s degree in CS, EE, or related field.</li>\n</ul>\n<p><strong>Preferred</strong></p>\n<ul>\n<li>Familiarity with orchestration and workflow technologies such as Ray, Kubeflow, Kueue, Istio, Knative, or Argo Workflows</li>\n</ul>\n<ul>\n<li>Deep expertise in Slurm/Kubernetes internals.</li>\n</ul>\n<ul>\n<li>Experience with distributed workloads, GPU-based applications, or ML pipelines.</li>\n</ul>\n<ul>\n<li>Knowledge of scheduling concepts like quota enforcement, pre-emption, and scaling strategies.</li>\n</ul>\n<ul>\n<li>Exposure to reliability practices including SLOs, alarms, and post-incident reviews.</li>\n</ul>\n<ul>\n<li>Experience with AI infrastructure and workloads (ML training, inference, or HPC).</li>\n</ul>\n<ul>\n<li>Ability to mentor senior engineers and elevate organizational standards.</li>\n</ul>\n<p><strong>Why CoreWeave?</strong></p>\n<p>At CoreWeave, we work hard, have fun, and move fast! We&#39;re in an exciting stage of hyper-growth that you will not want to miss out on.</p>\n<p>We&#39;re not afraid of a little chaos, and we&#39;re constantly learning.</p>\n<p>Our team cares deeply about how we build our product and how we work together, which is represented through our core values:</p>\n<ul>\n<li>Be Curious at Your Core</li>\n</ul>\n<ul>\n<li>Act Like an Owner</li>\n</ul>\n<ul>\n<li>Empower Employees</li>\n</ul>\n<ul>\n<li>Deliver Best-in-Class Client Experiences</li>\n</ul>\n<ul>\n<li>Achieve More Together</li>\n</ul>\n<p>We support and encourage an entrepreneurial outlook and independent thinking.</p>\n<p>We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems.</p>\n<p>As we get set for take off, the growth opportunities within the organization are constantly expanding.</p>\n<p>You will be surrounded by some of the best talent in the industry, who will want to learn from you, too.</p>\n<p>Come join us!</p>\n<p><strong>Salary and Benefits</strong></p>\n<p>The base salary range for this role is $185,000 to $275,000.</p>\n<p>The starting salary will be determined based on job-related knowledge, skills, experience, and market location.</p>\n<p>We strive for both market alignment and internal equity when determining compensation.</p>\n<p>In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).</p>\n<p><strong>What We Offer</strong></p>\n<p>The range we&#39;ve posted represents the typical compensation range for this role.</p>\n<p>To determine actual compensation, we review the market rate for each candidate which can include a variety of factors.</p>\n<p>These include qualifications, experience, interview performance, and location.</p>\n<p>In addition to a competitive salary, we offer a variety of benefits to support your needs, including:</p>\n<ul>\n<li>Medical, dental, and vision insurance - 100% paid for by CoreWeave</li>\n</ul>\n<ul>\n<li>Company-paid Life Insurance</li>\n</ul>\n<ul>\n<li>Voluntary supplemental life insurance</li>\n</ul>\n<ul>\n<li>Short and long-term disability insurance</li>\n</ul>\n<ul>\n<li>Flexible Spending Account</li>\n</ul>\n<ul>\n<li>Health Savings Account</li>\n</ul>\n<ul>\n<li>Tuition Reimbursement</li>\n</ul>\n<ul>\n<li>Ability to Participate in Employee Stock Purchase Program (ESPP)</li>\n</ul>\n<ul>\n<li>Mental Wellness Benefits through Spring Health</li>\n</ul>\n<ul>\n<li>Family-Forming support provided by Carrot</li>\n</ul>\n<ul>\n<li>Paid Parental Leave</li>\n</ul>\n<ul>\n<li>Flexible, full-service childcare support with Kinside</li>\n</ul>\n<ul>\n<li>401(k) with a generous employer match</li>\n</ul>\n<ul>\n<li>Flexible PTO</li>\n</ul>\n<ul>\n<li>Catered lunch each day in our office and data center locations</li>\n</ul>\n<ul>\n<li>A casual work environment</li>\n</ul>\n<ul>\n<li>A work culture focused on innovative disruption</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_96d05ee1-799","directApply":true,"hiringOrganization":{"@type":"Organization","name":"CoreWeave","sameAs":"https://www.coreweave.com","logo":"https://logos.yubhub.co/coreweave.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/coreweave/jobs/4658801006","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$185,000 to $275,000","x-skills-required":["software engineering","distributed systems","Slurm","Kubernetes","cloud-native development","Go","scheduling","quota enforcement","scaling strategies","reliability practices","SLOs","alarms","post-incident reviews","AI infrastructure","workloads","ML training","inference","HPC"],"x-skills-preferred":["orchestration and workflow technologies","Ray","Kubeflow","Kueue","Istio","Knative","Argo Workflows"],"datePosted":"2026-04-18T15:53:28.322Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bellevue, WA / Sunnyvale, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, distributed systems, Slurm, Kubernetes, cloud-native development, Go, scheduling, quota enforcement, scaling strategies, reliability practices, SLOs, alarms, post-incident reviews, AI infrastructure, workloads, ML training, inference, HPC, orchestration and workflow technologies, Ray, Kubeflow, Kueue, Istio, Knative, Argo Workflows","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":185000,"maxValue":275000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d50772ab-afe"},"title":"Staff / Senior Software Engineer, Cloud Inference","description":"<p>We are seeking a Staff / Senior Software Engineer to join our Cloud Inference team. The successful candidate will design and build infrastructure that serves Claude across multiple cloud service providers (CSPs), accounting for differences in compute hardware, networking, APIs, and operational models.</p>\n<p>The ideal candidate will have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users. 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Azure), with exposure to Kubernetes, Infrastructure as Code or container orchestration</li>\n</ul>\n<ul>\n<li>Strong interest in inference</li>\n</ul>\n<ul>\n<li>Thrive in cross-functional collaboration with both internal teams and external partners</li>\n</ul>\n<ul>\n<li>Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems</li>\n</ul>\n<ul>\n<li>Are highly autonomous and self-driven, taking ownership of problems end-to-end with a bias toward flexibility and high-impact work</li>\n</ul>\n<ul>\n<li>Pick up slack, even when it goes outside your job description</li>\n</ul>\n<p>Preferred skills:</p>\n<ul>\n<li>Direct experience working with CSP partner teams to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings</li>\n</ul>\n<ul>\n<li>A background in building platform-agnostic tooling or abstraction layers that work across cloud providers</li>\n</ul>\n<ul>\n<li>Hands-on experience with capacity management, cost optimisation, or resource planning at scale across heterogeneous environments</li>\n</ul>\n<ul>\n<li>Strong familiarity with LLM inference optimisation, batching, caching, and serving strategies</li>\n</ul>\n<ul>\n<li>Experience with Machine learning infrastructure including GPUs, TPUs, Trainium, or other AI accelerators</li>\n</ul>\n<ul>\n<li>Background designing and building CI/CD systems that automate deployment and validation across cloud environments</li>\n</ul>\n<ul>\n<li>Solid understanding of multi-region deployments, geographic routing, and global traffic management</li>\n</ul>\n<ul>\n<li>Proficiency in Python or Rust</li>\n</ul>\n<p>Salary Range: $300,000-$485,000 USD</p>\n<p>Experience Level: Staff</p>\n<p>Employment Type: Full-time</p>\n<p>Workplace Type: Hybrid</p>\n<p>Category: Engineering</p>\n<p>Industry: Technology</p>\n<p>Required Skills:</p>\n<ul>\n<li>High-performance, large-scale 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tooling</li>\n</ul>\n<ul>\n<li>Hands-on experience with capacity management</li>\n</ul>\n<ul>\n<li>Strong familiarity with LLM inference optimisation</li>\n</ul>\n<ul>\n<li>Experience with Machine learning infrastructure</li>\n</ul>\n<ul>\n<li>Background designing and building CI/CD systems</li>\n</ul>\n<ul>\n<li>Solid understanding of multi-region deployments</li>\n</ul>\n<ul>\n<li>Proficiency in Python or Rust</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d50772ab-afe","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5107466008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$300,000-$485,000 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rust"],"datePosted":"2026-04-18T15:53:24.048Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"engineering","industry":"technology","skills":"high-performance, large-scale distributed systems, cloud computing (AWS, GCP, Azure), kubernetes, infrastructure as code, container orchestration, inference, cross-functional collaboration, autonomy and self-driven, platform-agnostic tooling, capacity management, cost optimisation, resource planning, llm inference optimisation, machine learning infrastructure, ci/cd systems, multi-region deployments, geographic routing, global traffic management, python, rust, direct experience working with csp partner teams, building platform-agnostic tooling, hands-on experience with capacity management, strong familiarity with llm inference optimisation, experience with machine learning infrastructure, background designing and building 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This leader will shape and drive the analytics strategy, mentor and develop the team, and collaborate cross-functionally with Marketing, Product and Engineering to translate business needs into actionable insights.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Team Leadership &amp; Mentorship: Hire, lead, coach, and develop a high-performing team of product and marketing analysts.</li>\n<li>Analytics Strategy &amp; Execution: Own the company&#39;s marketing measurement, experimentation, and insight generation for paid, owned, and integrated channels, and for performance across acquisition, activation, engagement, retention, and LTV.</li>\n<li>Hands-On Analysis: Perform deep-dive analyses to uncover insights that inform product and business decisions.</li>\n<li>Cross-Functional Collaboration: Act as a strategic thought partner to identify opportunities, measure success, and optimize Marketing and product performance.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>10+ years of experience in analytics, with at least 2 years in a people management role.</li>\n<li>Strong technical skills in SQL, Python, data visualization tools (Amplitude, Tableau) and experimentation.</li>\n<li>Has direct experience in marketing analytics using Media Mix Models (MMM), difference-in-differences and other causal inference techniques.</li>\n<li>Proven track record of leading high-performing marketing or product analytics teams.</li>\n<li>Excellent communication and storytelling skills.</li>\n<li>Ability to balance strategic thinking with hands-on execution.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_af1b7253-e81","directApply":true,"hiringOrganization":{"@type":"Organization","name":"EarnIn","sameAs":"https://www.earnin.com/","logo":"https://logos.yubhub.co/earnin.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/earnin/jobs/7729952","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$215,000 to $263,000","x-skills-required":["SQL","Python","data visualization tools (Amplitude, Tableau)","experimentation","Media Mix Models (MMM)","difference-in-differences","causal inference techniques"],"x-skills-preferred":["fintech","consumer tech","data-driven product organization","modern data stacks (e.g., Databricks, Tableau, Amplitude)","influencing executive stakeholders","cross-functional initiatives"],"datePosted":"2026-04-18T15:53:15.064Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Marketing","industry":"Finance","skills":"SQL, Python, data visualization tools (Amplitude, Tableau), experimentation, Media Mix Models (MMM), difference-in-differences, causal inference techniques, fintech, consumer tech, data-driven product organization, modern data stacks (e.g., Databricks, Tableau, Amplitude), influencing executive stakeholders, cross-functional initiatives","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":215000,"maxValue":263000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5f6e6eac-370"},"title":"Sr GPU Infrastructure Software Engineer","description":"<p>CoreWeave is seeking a Senior GPU Infrastructure Software Engineer to join our team. As a senior engineer, you will be responsible for leading designs, raising engineering standards, and delivering measurable improvements to latency, throughput, and reliability across multiple services. You will partner with fleet, product, and hardware teams to evolve our GPU performance testing platform to ensure we deliver a reliable and performant experience to our customers.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Design and implement solutions to problems of scale for testing and validation of CoreWeave&#39;s global infrastructure.</li>\n<li>Design and develop Kubernetes-native controllers and operators to automate infrastructure workflows.</li>\n<li>Build and maintain scalable backend services and APIs (gRPC/REST) in Go or Python.</li>\n<li>Develop performance tests and automation workflows to expand hardware validation across the CoreWeave fleet.</li>\n<li>Write and maintain Kubernetes custom controllers and operators to automate infrastructure testing.</li>\n<li>Adapt and extend open source tooling to enhance visibility into system metrics, performance, and health.</li>\n</ul>\n<p>To be successful in this role, you should have:</p>\n<ul>\n<li>~5 to 8 years experience.</li>\n<li>Proficiency in Go and/or Python software development.</li>\n<li>Hands-on experience with writing Kubernetes operators/controllers.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience testing hardware at scale.</li>\n<li>HPC Experience.</li>\n<li>Experience with AI/ML infrastructure and training / inference.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_5f6e6eac-370","directApply":true,"hiringOrganization":{"@type":"Organization","name":"CoreWeave","sameAs":"https://www.coreweave.com","logo":"https://logos.yubhub.co/coreweave.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/coreweave/jobs/4627277006","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$165,000 to $242,000","x-skills-required":["Go","Python","Kubernetes","GPU performance testing","infrastructure validation"],"x-skills-preferred":["HPC Experience","AI/ML infrastructure","training / inference"],"datePosted":"2026-04-18T15:53:07.770Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Sunnyvale, CA / Bellevue, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Go, Python, Kubernetes, GPU performance testing, infrastructure validation, HPC Experience, AI/ML infrastructure, training / inference","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":165000,"maxValue":242000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_74be15a1-bce"},"title":"Software Engineer, Inference Deployment","description":"<p>Our mandate is to make inference deployment boring and unattended. We serve Claude to millions of users across GPUs, TPUs, and Trainium , and every model update must reach production safely, quickly, and without disrupting service. As a Software Engineer on the Launch Engineering team, you&#39;ll design and build the deployment infrastructure that moves inference code from merge to production.</p>\n<p>This is a resource-constrained optimization problem at its core: validation and deployment consume the same accelerator chips that serve customer traffic , your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, so the system must adapt continuously. You&#39;ll build systems that navigate these constraints , orchestrating validation, scheduling deployments intelligently, and driving down cycle time from merge to production.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions</li>\n</ul>\n<ul>\n<li>Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes</li>\n</ul>\n<ul>\n<li>Extend deployment observability , dashboards and tooling that answer &quot;what code is running in production,&quot; &quot;where is my commit,&quot; and &quot;what validation passed for this deploy&quot;</li>\n</ul>\n<ul>\n<li>Drive down cycle time from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism</li>\n</ul>\n<ul>\n<li>Optimize fleet rollout strategies for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity</li>\n</ul>\n<ul>\n<li>Evolve self-service model onboarding so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement</li>\n</ul>\n<ul>\n<li>Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems</li>\n</ul>\n<p>You May Be a Good Fit If You Have:</p>\n<ul>\n<li>5+ years of experience building deployment, release, or delivery infrastructure at scale</li>\n</ul>\n<ul>\n<li>Strong software engineering skills with experience designing systems that manage complex state machines and multi-stage pipelines</li>\n</ul>\n<ul>\n<li>Experience with deployment systems where resource constraints shape the design , whether that&#39;s fleet capacity, network bandwidth, hardware availability, or coordinated rollout windows</li>\n</ul>\n<ul>\n<li>A track record of building automation that measurably improves deployment velocity and reliability</li>\n</ul>\n<ul>\n<li>Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration</li>\n</ul>\n<ul>\n<li>Comfort working across the stack , from backend services and databases to CLI tools and web UIs</li>\n</ul>\n<ul>\n<li>Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners</li>\n</ul>\n<p>Strong Candidates May Also Have:</p>\n<ul>\n<li>Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium)</li>\n</ul>\n<ul>\n<li>Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity)</li>\n</ul>\n<ul>\n<li>Experience with progressive delivery in systems with long validation cycles: canary/soak 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How can we trust them?&quot; \\n \\n The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. \\n \\n Think of us as doing &quot;neuroscience&quot; of neural networks using &quot;microscopes&quot; we build - or reverse-engineering neural networks like binary programs. \\n \\n More resources to learn about our work: \\n - Our research blog - covering advances including Monosemantic Features and Circuits \\n - An Introduction to Interpretability from our research lead, Chris Olah \\n - The Urgency of Interpretability from CEO Dario Amodei \\n - Engineering Challenges Scaling Interpretability - directly relevant to this role \\n - 60 Minutes segment - Around 8:07, see a demo of tooling our team built \\n - New Yorker article - what it&#39;s like to work on one of AI&#39;s hardest open problems \\n \\n Even if you haven&#39;t worked on interpretability before, the infrastructure expertise is similar to what&#39;s needed across the lifecycle of a production language model: \\n - Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips \\n - Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model&#39;s internal activations mid-forward-pass - for example, adding a &quot;steering vector&quot; \\n - Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission \\n \\n The science keeps scaling - and it&#39;s now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI. \\n \\n RESPONSIBILITIES: \\n - Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector application \\n - Resolve scaling and efficiency bottlenecks through profiling, optimization, and close collaboration with peer infrastructure teams \\n - Design tools, abstractions, and platforms that enable researchers to rapidly experiment without hitting engineering barriers \\n - Help bring interpretability research into production safety audits - with real deadlines and high reliability expectations \\n - Work across the stack - from model internals and accelerator-level optimization to user-facing research tooling \\n \\n YOU MAY BE A GOOD FIT IF YOU: \\n - Have 5-10+ years of experience building software \\n - Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python \\n - Are extremely curious about unfamiliar domains; 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This includes Slurm, Kubernetes, SUNK, and the control planes that support AI training, inference, and model onboarding at scale.</p>\n<p>You will define long-term architecture, solve hard scaling problems, and set technical direction across teams. Your work will directly affect how quickly customers can run models, how efficiently we use GPUs, and how reliably the platform behaves at scale.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Defining the long-term architecture for CoreWeave&#39;s orchestration platforms across Kubernetes, Slurm, SUNK, Kueue, and related systems.</li>\n<li>Acting as a technical authority on scheduling, quota enforcement, fairness, pre-emption, and multi-tenant GPU isolation.</li>\n<li>Making design decisions that balance performance, reliability, cost, and operational complexity.</li>\n</ul>\n<p>In addition to these responsibilities, you will also lead the evolution of Kubernetes-native control planes, including SUNK and custom operators, and design systems that support workload admission, validation, and rollout, including model onboarding flows.</p>\n<p>You will work closely with cross-functional teams to ensure that the systems you design and implement meet the needs of our customers and are scalable, reliable, and efficient.</p>\n<p>If you have a passion for building large-scale distributed systems and are looking for a challenging and rewarding role, we encourage you to apply.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_0f249232-d14","directApply":true,"hiringOrganization":{"@type":"Organization","name":"CoreWeave","sameAs":"https://www.coreweave.com","logo":"https://logos.yubhub.co/coreweave.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/coreweave/jobs/4658799006","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$206,000 to $303,000","x-skills-required":["Kubernetes","Slurm","SUNK","Go","Cloud-native systems development","GPU-heavy platforms for AI training, inference, or HPC workloads"],"x-skills-preferred":["Kueue","Kubeflow","Argo Workflows","Ray","Istio","Knative"],"datePosted":"2026-04-18T15:48:07.140Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bellevue, WA / Sunnyvale, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Kubernetes, Slurm, SUNK, Go, Cloud-native systems development, GPU-heavy platforms for AI training, inference, or HPC workloads, Kueue, Kubeflow, Argo Workflows, Ray, Istio, Knative","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":206000,"maxValue":303000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3ff860ce-94c"},"title":"Staff, Advanced Analytics, CS Safety","description":"<p>We are looking for a Staff Advanced Analyst to help Airbnb enable travel for our millions of guests and hosts on our platform. This role will sit under the Advanced Analytics family and support Product and Business leaders within our CS Safety organisation.</p>\n<p>As a Staff Advanced Analyst, you will be a data thought partner to product and business leaders across teams through providing insights, recommendations, and enabling data-informed decisions. You will drive day-to-day analytics and create scalable data tools, identify pain points in travelling and hosting, and work with product leadership to improve experiences for our guest, host, and agent community.</p>\n<p>In addition, you will leverage Airbnb&#39;s rich and unique data, state-of-the-art machine learning infrastructure, and other central data science tools to build and grow the measurement capacity within the organisation. You will also be deeply involved in the technical details of the various systems we build, and will have the opportunity to collaborate with a strong team of engineers, product managers, designers, and operations agents to achieve shared, cross-functional goals to help keep Airbnb&#39;s community safe and trusted.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Leading and driving data-driven roadmaps for the CS Safety working groups</li>\n<li>Recommending actionable solutions backed by data and metrics to product and operational problems</li>\n<li>Building and owning an insights and reporting platform that measures and improves the effectiveness of behaviours, product interfaces, and processes across the CS Safety platform and contact centre network</li>\n<li>Performing data modelling of the various entities using tools and frameworks for optimising community and agent experiences</li>\n<li>Defining and evaluating key metrics in an unstructured problem space, including measurement of the ML models that drive product development</li>\n<li>Anticipating emerging safety risks through early-warning indicators, trend analysis, predictive modelling, and scenario planning to assess operational risk</li>\n<li>Influencing data-driven decisions across business verticals in day-to-day via business reviews, scorecards, self-serve portal, OKRs, and planning among others</li>\n<li>Influencing experimentation and measurement strategies; conducting power analyses, defining exit criteria, and using statistical models to improve inference</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>A minimum of 10+ years of industry experience in business analytics and a degree (Masters or PhD) in a quantitative field (e.g., Statistics, Econometrics, Computer Science, Engineering, Mathematics, Data Science, Operations Research)</li>\n<li>Experience supporting safety, risk, Trust &amp; Safety, compliance, or employee wellbeing in high-volume call centre or customer operations environments</li>\n<li>Expert skills in SQL and expert in at least one programming language for data analysis (Python or R)</li>\n<li>Experience with non-experimental causal inference methods, experimentation, and machine learning techniques, ideally in a multi-sided platform setting</li>\n<li>Working knowledge of schema design and high-dimensional data modelling (ETL framework like Airflow)</li>\n<li>Ability to work under conditions of ambiguity in a fast-growth, sometimes uncertain and complex environment</li>\n<li>Comfortable operating independently with minimal planning, direction, and supervision</li>\n<li>Proven track record of influencing senior leaders and driving outcomes</li>\n</ul>\n<p>Experience Level: Staff Employment Type: Full-time Workplace Type: Remote Category: Engineering Industry: Technology Salary Range: $176,000-$220,000 USD Required Skills: SQL, Python, R, Machine Learning, Data Analysis, Data Modelling, Causal Inference, Experimentation, Statistical Models Preferred Skills: Data Science, Operations Research, Statistics, Econometrics, Computer Science, Engineering, Mathematics</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_3ff860ce-94c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Airbnb","sameAs":"https://www.airbnb.com/","logo":"https://logos.yubhub.co/airbnb.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/airbnb/jobs/7579193","x-work-arrangement":"remote","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$176,000-$220,000 USD","x-skills-required":["SQL","Python","R","Machine Learning","Data Analysis","Data Modelling","Causal Inference","Experimentation","Statistical Models"],"x-skills-preferred":["Data Science","Operations Research","Statistics","Econometrics","Computer Science","Engineering","Mathematics"],"datePosted":"2026-04-18T15:47:55.305Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"engineering","industry":"technology","skills":"SQL, Python, R, Machine Learning, Data Analysis, Data Modelling, Causal Inference, Experimentation, Statistical Models, Data Science, Operations Research, Statistics, Econometrics, Computer Science, Engineering, Mathematics","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":176000,"maxValue":220000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1051437d-3f6"},"title":"Senior Data Scientist, Consumer","description":"<p>We are seeking a Senior Data Scientist to join our Consumer Data Science team. As a Senior Data Scientist, you will play a significant role in driving the success of key product areas at Reddit, including Consumer, Ads, and Safety. You will lead and contribute to defining the product strategy through measurement and metrics design, experimentation and causal analyses, supporting product decisions via deep data analyses, and research.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Develop action-oriented insights to drive the product strategy through observational causal analysis and experiment meta-analysis, and clearly communicate results to stakeholders up to the C-suite to take action based on the recommendations</li>\n<li>Uplevel experimentation practices on the team through guiding design, execution, and deep dive analyses to maximize learnings from A/B tests</li>\n<li>Create new ETLs, tables, dashboards, and other self-serve tools to enable other data scientists and cross-functional partners to find and interact with data seamlessly</li>\n<li>Design, evaluate, and/or measure team-level KPIs to enable quarterly goal setting and demonstrate team impact</li>\n<li>Regularly engage with stakeholders to gather feedback and share progress on work at all stages to ensure alignment between DS and other teams on business goals and outcomes</li>\n<li>Mentor more junior data scientists and business partners in data science best practices and methods to increase data literacy and improve decision making</li>\n</ul>\n<p>Required Qualifications:</p>\n<ul>\n<li>Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research</li>\n<li>For M.S. holders: 5+ years of industry experience in applied science or data science roles</li>\n<li>For Ph.D. holders: 4+ years of industry experience in applied science or data science roles</li>\n<li>Expertise in querying relational databases (SQL) and programming languages (e.g., R / Python)</li>\n<li>Deep understanding of online experimentation (A/B testing), causal inference, and statistical techniques</li>\n<li>Comfortable in innovative and fast-paced environments, and a bias toward action</li>\n<li>Strong technical communication / data storytelling skills and demonstrated ability to discuss complex topics with technical and non-technical audiences alike</li>\n<li>Able to tackle ambiguous and undefined problems by creating an action plan, getting feedback, and iterating</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p>Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/. To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is $190,800-$267,100 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_1051437d-3f6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7275414","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$190,800-$267,100 USD","x-skills-required":["SQL","R","Python","online experimentation","causal inference","statistical techniques"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:47:47.513Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"SQL, R, Python, online experimentation, causal inference, statistical techniques","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":190800,"maxValue":267100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3480e0e8-2e9"},"title":"Senior Data Scientist, Ads","description":"<p>We are looking for a highly motivated and experienced Senior Data Scientist to join our growing Ads Data Science team. As a Senior Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve the adoption and performance of our advertising platform through data-driven insights.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit&#39;s ad platform</li>\n<li>Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform</li>\n<li>Collaborate with product managers and engineers to define product requirements and translate them into data science solutions</li>\n<li>Develop ML models &amp; DS methods to improve anomaly detection, prediction, &amp; pattern recognition</li>\n<li>Communicate findings and recommendations to stakeholders across the organization</li>\n<li>Stay up-to-date on the latest advancements in machine learning and data science</li>\n<li>Mentor and guide junior data scientists on the team</li>\n</ul>\n<p>Qualifications:</p>\n<ul>\n<li>Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research</li>\n<li>For M.S. holders: 5+ years of industry experience in applied science or data science roles</li>\n<li>For Ph.D. holders: 4+ years of industry experience in applied science or data science roles</li>\n<li>Platform experience and a deep understanding of the ads ecosystem</li>\n<li>Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design</li>\n<li>Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus</li>\n<li>Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch</li>\n<li>Experience with SQL and relational databases</li>\n<li>Excellent communication and presentation skills</li>\n</ul>\n<p>Bonus Points:</p>\n<ul>\n<li>Experience with online advertising and ad tech</li>\n<li>Experience with causal inference and A/B testing</li>\n<li>Contributions to open-source projects or publications in relevant conferences or journals</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_3480e0e8-2e9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/6042236","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$190,800-$267,100 USD","x-skills-required":["Python","R","Spark","Hadoop","BigQuery","scikit-learn","TensorFlow","PyTorch","SQL","relational databases","statistical modeling","machine learning algorithms","causal inference","experimental design"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:47:41.569Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, R, Spark, Hadoop, BigQuery, scikit-learn, TensorFlow, PyTorch, SQL, relational databases, statistical modeling, machine learning algorithms, causal inference, experimental design","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":190800,"maxValue":267100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_40a343d1-388"},"title":"Senior Data Scientist, Platform (Algorithms/Trust)","description":"<p>We&#39;re looking for a Senior Data Scientist to join our Content Integrity Data Science team. As a Senior Data Scientist, you will play a key role in building and protecting trust on the Airbnb platform by ensuring that the content on Airbnb, including listings, profiles, messages and other user-generated experiences, is accurate, authentic and aligned with our policies and community standards.</p>\n<p>This role is unique in that it directly improves the safety, trust, and quality of real-world user experiences by advancing Airbnb&#39;s ability to understand, interpret and act on content at scale. You will help shape how the platform reasons about listings, profiles, messages and other user-generated content by building the next generation of Trust Content Understanding Models.</p>\n<p>The ideal candidate is a motivated and talented &#39;full-stack&#39; Data Scientist with strong applied ML intuition and a bias toward impact, who can own and drive forward challenging, high-visibility initiatives such as:</p>\n<ul>\n<li>Advance Airbnb&#39;s content integrity capabilities by building Natural Language Processing (NLP) and LLM-based models that understand intent, policy compliance, quality and risk across listings, profiles, and user communications</li>\n</ul>\n<ul>\n<li>Develop high-performing models for detecting problematic or misleading content, including text classification, semantic similarity, information extraction and generative model-based reasoning for policy interpretation and enforcement</li>\n</ul>\n<ul>\n<li>Design and optimize human-in-the-loop Machine Learning (ML) systems for content review, labeling, escalation and continuous model improvement</li>\n</ul>\n<ul>\n<li>Build systems to detect emerging content risks and abuse patterns across regions, cohorts and surfaces using statistical, ML and representation-learning approaches</li>\n</ul>\n<ul>\n<li>Design intelligent sampling and evaluation strategies to measure rare events, policy recall, false positives/negatives and model blind spots in large-scale content systems</li>\n</ul>\n<p>A Typical Day:</p>\n<ul>\n<li>Artificial Intelligence / Machine Learning: Build and deploy production AI/ML systems for content integrity and trust content understanding, including feature engineering, model development and evaluation, thresholding, error analysis and end-to-end model lifecycle management. This includes working with NLP and LLM-based models in real production settings.</li>\n</ul>\n<ul>\n<li>Inference: Partner with inference data scientists to conduct rigorous quantitative analyses, applying working knowledge of causal inference to interpret results, assess impact, and identify gaps and opportunities to improve content quality and trust outcomes.</li>\n</ul>\n<ul>\n<li>Optimization: Develop frameworks to analyze tradeoffs between enforcement accuracy, user experience, operational cost and coverage, and propose strategies to optimize overall system effectiveness.</li>\n</ul>\n<ul>\n<li>Communication &amp; Collaboration: Deliver robust research reports with effective data visualizations, clear storytelling and bullet-proof accuracy to drive forward impact in collaboration with cross-functional partners in product, engineering and operations</li>\n</ul>\n<ul>\n<li>Empowerment: Think strategically about how to scale and evolve Airbnb&#39;s content integrity defenses, helping define the long-term vision for the role of AI-driven content understanding across the Trust ecosystem.</li>\n</ul>\n<p>Your Expertise:</p>\n<ul>\n<li>5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (computer science, statistics etc.), or 2+ years of experience with a Ph.D.</li>\n</ul>\n<ul>\n<li>State-of-the-art knowledge of AI/ML models</li>\n</ul>\n<ul>\n<li>Hands-on experience building, evaluating, and deploying NLP and LLM-based solutions, including text classification, information extraction, semantic understanding or generative applications.</li>\n</ul>\n<ul>\n<li>Working knowledge of causal inference</li>\n</ul>\n<ul>\n<li>Skilled in statistical programming (Python or R) and database usage (SQL)</li>\n</ul>\n<ul>\n<li>Proven ability to communicate clearly and effectively to audiences of varying technical levels</li>\n</ul>\n<ul>\n<li>Ability to translate complex findings and results into compelling narratives that drive impact</li>\n</ul>\n<ul>\n<li>Excellent project management, communication, and collaboration skills</li>\n</ul>\n<ul>\n<li>Trust &amp; Safety experience is a plus</li>\n</ul>\n<p>Your Location:</p>\n<p>This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from.</p>\n<p>Our Commitment To Inclusion &amp; Belonging:</p>\n<p>Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com. Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.</p>\n<p>How We&#39;ll Take Care of You:</p>\n<p>Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.</p>\n<p>Pay Range $177,000-$208,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_40a343d1-388","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Airbnb","sameAs":"https://www.airbnb.com/","logo":"https://logos.yubhub.co/airbnb.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/airbnb/jobs/7594971","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$177,000-$208,000 USD","x-skills-required":["Natural Language Processing (NLP)","LLM-based models","text classification","semantic similarity","information extraction","generative model-based reasoning","policy interpretation and enforcement","human-in-the-loop Machine Learning (ML)","content review","labeling","escalation","continuous model improvement","statistical","representation-learning approaches","intelligent sampling","evaluation strategies","rare events","policy recall","false positives/negatives","model blind spots","large-scale content systems","Python","R","SQL","causal inference","statistical programming","database usage"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:47:24.814Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Natural Language Processing (NLP), LLM-based models, text classification, semantic similarity, information extraction, generative model-based reasoning, policy interpretation and enforcement, human-in-the-loop Machine Learning (ML), content review, labeling, escalation, continuous model improvement, statistical, representation-learning approaches, intelligent sampling, evaluation strategies, rare events, policy recall, false positives/negatives, model blind spots, large-scale content systems, Python, R, SQL, causal inference, statistical programming, database usage","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":177000,"maxValue":208000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_779cd718-611"},"title":"Principal Data Scientist, Ads","description":"<p>We&#39;re seeking a highly motivated Principal Data Scientist to drive the strategic application of advanced quantitative methods across our advertising platform.</p>\n<p>In this pivotal leadership role, you will define and implement the next generation of foundational data science solutions, leveraging expertise in statistics, econometrics, machine learning, and other quantitative methods to optimize Reddit&#39;s Ads marketplace.</p>\n<p>As a thought leader, you will champion scientific rigor, causal inference, and economic modeling, while providing deep mentorship to the broader team.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Defining the future of Ads Data Science and owning the design and long-term evolution of our core Ads Data Science solutions and infrastructure</li>\n<li>Identifying fundamental gaps and opportunities in our current systems and leading the strategic design and scientific roadmap for new solutions</li>\n<li>Taking end-to-end ownership of complex problem domains such as full funnel acceleration, advertiser lifetime value (LTV), and developing advanced predictive and causal frameworks</li>\n<li>Establishing scientific standards and defining best practices for large-scale statistical modeling, economic analysis, causal inference, offline model evaluation, and A/B experimentation</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>Demonstrated expertise in at least one of the following: ads marketplace understanding, auctioning/bidding, ads creative &amp; format evaluation, measurement &amp; experimentation at scale</li>\n<li>Master&#39;s or Ph.D. in Economics, Statistics, Computer Science, Operations Research, or a related quantitative discipline</li>\n<li>Advanced proficiency in statistical programming (Python or R) and SQL</li>\n<li>Experience with statistical analysis, economic modeling, foundational machine learning and/or optimization techniques</li>\n<li>Strong understanding of experimental design, causal inference, or A/B testing methodologies</li>\n</ul>\n<p>Benefits include comprehensive healthcare benefits, income replacement programs, 401k with employer match, global benefit programs, family planning support, gender-affirming care, mental health &amp; coaching benefits, flexible vacation &amp; paid volunteer time off, and generous paid parental leave.</p>\n<p>The base salary range for this position is $268,000-$365,100 USD.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_779cd718-611","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7330347","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$268,000-$365,100 USD","x-skills-required":["ads marketplace understanding","auctioning/bidding","ads creative & format evaluation","measurement & experimentation at scale","statistical programming (Python or R)","SQL","statistical analysis","economic modeling","foundational machine learning","optimization techniques","experimental design","causal inference","A/B testing methodologies"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:47:02.115Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"ads marketplace understanding, auctioning/bidding, ads creative & format evaluation, measurement & experimentation at scale, statistical programming (Python or R), SQL, statistical analysis, economic modeling, foundational machine learning, optimization techniques, experimental design, causal inference, A/B testing methodologies","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":268000,"maxValue":365100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8a4223c5-8fd"},"title":"Sr. Data Science Manager - Ads Marketplace","description":"<p>We&#39;re seeking a results-oriented and strategically minded Data Science Leader to work with our Ads Marketplace teams. As a Senior Data Science Manager, you will lead a team of high-calibre data scientists to partner with Product, Engineering, and Sales to build a world-class, transparent, and efficient marketplace.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Inspire, lead, and grow a team of data scientists to achieve our longer-term vision</li>\n<li>Drive data science projects end-to-end in partnership with Product, Engineering, and other partners to inform product strategy and investment decisions</li>\n<li>Analyze large datasets to identify trends, patterns, and insights that can help understand marketplace dynamics and help cross-functional teams (e.g., product, engineering, marketing) to define and execute data-driven optimization strategies</li>\n<li>Actively influence the design of the strategy and shaping of the roadmap. Generate and use team insights to set and prioritise longer-term goals</li>\n<li>Create and implement A/B testing, experimentation, and other cutting-edge statistical/mathematical frameworks to analyse Ads (marketplace) performance</li>\n<li>Continually develop &amp; execute on a Data Science roadmap and vision for your team</li>\n<li>Stay abreast of industry best practices and emerging technologies in the field of advertising and data science</li>\n<li>Foster a culture of innovation, collaboration, and technical excellence</li>\n<li>Be an integral part of the Data Science Org, leveraging and contributing to the vibrant knowledge base, shared across a community of world-class data experts</li>\n</ul>\n<p>Required Qualifications:</p>\n<ul>\n<li>PhD or Master&#39;s degree in Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields</li>\n<li>4+ years of management experience. Experience managing managers is a bonus</li>\n<li>Strong skills in programming (Python or R) and SQL</li>\n<li>Experience in leveraging AI-assisted development tools (e.g., Cursor, Claude, or similar LLMs) to improve personal and team productivity, code quality, and technical problem-solving</li>\n<li>Deep understanding of Ads Marketplace</li>\n<li>Extensive experience of online experimentation and causal inference</li>\n<li>Ability to communicate &amp; discuss low-level technical topics as well as high-level strategies</li>\n<li>Comfortable in innovative and fast-paced environments, and an innate ability to bias toward action</li>\n<li>Results-oriented with a strong customer and business focus</li>\n<li>Track-record driving product roadmap and execution with strong cross-org collaborations</li>\n<li>Ability to provide structure and tackle ambiguous and undefined problems</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_8a4223c5-8fd","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7749073","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$232,500-$325,500 USD","x-skills-required":["PhD or Master's degree in Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields","4+ years of management experience","Strong skills in programming (Python or R) and SQL","Experience in leveraging AI-assisted development tools","Deep understanding of Ads Marketplace","Extensive experience of online experimentation and causal inference"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:46:35.277Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD or Master's degree in Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields, 4+ years of management experience, Strong skills in programming (Python or R) and SQL, Experience in leveraging AI-assisted development tools, Deep understanding of Ads Marketplace, Extensive experience of online experimentation and causal inference","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":232500,"maxValue":325500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_f723a069-05a"},"title":"Engineering Manager, Notifications Relevance","description":"<p>We are looking for an Engineering Manager to lead our Notifications Relevance team, shaping the future of Notifications at Reddit. In this role, you will lead a team of machine learning engineers dedicated to advancing our current Notifications Relevance systems.</p>\n<p>This is a high-impact team driving DAU growth and long-term user retention by connecting users to what matters most to them. If applying ML / AI in production to improve the relevance of Reddit Notifications excites you, then you’ve found the right place.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead the team that architects and designs notifications relevance at Reddit.</li>\n<li>Guide team on holistic, adaptive systems covering budgeting optimization, candidate retrieval, and ranking.</li>\n<li>Work with ML engineers to design, implement, and optimize machine-learning models that drive personalization and user re-engagement.</li>\n<li>Participate in the full development cycle: design, develop, QA, experiment, analyze, and deploy.</li>\n<li>Build and maintain a diverse team that can collaborate across disciplines to find technical solutions to complex challenges.</li>\n<li>Serve as a thought partner to product and upper management to ensure your team’s plans align with company goals.</li>\n<li>Communicate your team’s work and set expectations with cross-functional stakeholders.</li>\n<li>Help your engineers identify career goals and create development plans to achieve them.</li>\n<li>Constantly seek opportunities to push your engineers &amp; managers outside their comfort zone and turn followers into leaders.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>2+ years of experience building and managing engineering teams.</li>\n<li>5+ years of experience as a Machine Learning Engineer or Software Engineer working on large-scale machine learning systems.</li>\n<li>Deep understanding of building and deploying large-scale recommender systems (retrieval + ranking) in production.</li>\n<li>Hands-on experience working with deep learning models, sequential features and real-time systems.</li>\n<li>Experience with distributed training and inference using tools like Ray, PyTorch Distributed, or similar.</li>\n<li>Familiarity with reinforcement learning or multi-objective optimization in recommendation systems.</li>\n<li>Entrepreneurial and self-directed, innovative, results-oriented, biased towards action in fast-paced environments.</li>\n<li>Able to communicate and discuss complex topics with technical and non-technical audiences.</li>\n<li>Able to tackle ambiguous and undefined problems.</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f723a069-05a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7340793","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$230,000-$322,000 USD","x-skills-required":["Machine Learning Engineer","Software Engineer","Deep Learning Models","Sequential Features","Real-Time Systems","Distributed Training","Inference","Reinforcement Learning","Multi-Objective Optimization"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:46:22.742Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning Engineer, Software Engineer, Deep Learning Models, Sequential Features, Real-Time Systems, Distributed Training, Inference, Reinforcement Learning, Multi-Objective Optimization","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":322000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_faffae87-882"},"title":"Staff Software Engineer - GenAI Performance and Kernel","description":"<p>As a staff software engineer for GenAI Performance and Kernel, you will own the design, implementation, optimization, and correctness of the high-performance GPU kernels powering our GenAI inference stack. You will lead development of highly-tuned, low-level compute paths, manage trade-offs between hardware efficiency and generality, and mentor others in kernel-level performance engineering.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Leading the design, implementation, benchmarking, and maintenance of core compute kernels optimized for various hardware backends (GPU, accelerators)</li>\n<li>Driving the performance roadmap for kernel-level improvements: vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, auto-tuning, etc.</li>\n<li>Integrating kernel optimizations with higher-level ML systems</li>\n<li>Building and maintaining profiling, instrumentation, and verification tooling to detect correctness, performance regressions, numerical issues, and hardware utilization gaps</li>\n<li>Leading performance investigations and root-cause analysis on inference bottlenecks, e.g. memory bandwidth, cache contention, kernel launch overhead, tensor fragmentation</li>\n<li>Establishing coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend portability, and maintainability</li>\n<li>Influencing system architecture decisions to make kernel improvements more effective (e.g. memory layout, dataflow scheduling, kernel fusion boundaries)</li>\n<li>Mentoring and guiding other engineers working on lower-level performance, providing code reviews, and helping set best practices</li>\n<li>Collaborating with infrastructure, tooling, and ML teams to roll out kernel-level optimizations into production, and monitoring their impact</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>BS/MS/PhD in Computer Science, or a related field</li>\n<li>Deep hands-on experience writing and tuning compute kernels (CUDA, Triton, OpenCL, LLVM IR, assembly or similar sort) for ML workloads</li>\n<li>Strong knowledge of GPU/accelerator architecture: warp structure, memory hierarchy (global, shared, register, L1/L2 caches), tensor cores, scheduling, SM occupancy, etc.</li>\n<li>Experience with advanced optimization techniques: tiling, blocking, software pipelining, vectorization, fusion, loop transformations, auto-tuning</li>\n<li>Familiarity with ML-specific kernel libraries (cuBLAS, cuDNN, CUTLASS, oneDNN, etc.) or open kernels</li>\n<li>Strong debugging and profiling skills (Nsight, NVProf, perf, vtune, custom instrumentation)</li>\n<li>Experience reasoning about numerical stability, mixed precision, quantization, and error propagation</li>\n<li>Experience in integrating optimized kernels into real-world ML inference systems; exposure to distributed inference pipelines, memory management, and runtime systems</li>\n<li>Experience building high-performance products leveraging GPU acceleration</li>\n<li>Excellent communication and leadership skills , able to drive design discussions, mentor colleagues, and make trade-offs visible</li>\n<li>A track record of shipping performance-critical, high-quality production software</li>\n<li>Bonus: published in systems/ML performance venues (e.g. MLSys, ASPLOS, ISCA, PPoPP), experience with custom accelerators or FPGA, experience with sparsity or model compression techniques</li>\n</ul>\n<p>The pay range for this role is $190,900-$232,800 USD per year, depending on location and experience.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_faffae87-882","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8202700002","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$190,900-$232,800 USD per year","x-skills-required":["Compute kernels","GPU/accelerator architecture","Advanced optimization techniques","ML-specific kernel libraries","Debugging and profiling skills","Numerical stability","Mixed precision","Quantization","Error propagation","Distributed inference pipelines","Memory management","Runtime systems","High-performance products","GPU acceleration"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:46:07.442Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Compute kernels, GPU/accelerator architecture, Advanced optimization techniques, ML-specific kernel libraries, Debugging and profiling skills, Numerical stability, Mixed precision, Quantization, Error propagation, Distributed inference pipelines, Memory management, Runtime systems, High-performance products, GPU acceleration","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":190900,"maxValue":232800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_97212bdf-dd1"},"title":"Research Engineer, Interpretability","description":"<p>Job Title: Research Engineer, Interpretability</p>\n<p>About the Role:</p>\n<p>When you see what modern language models are capable of, do you wonder, &quot;How do these things work? How can we trust them?&quot; The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe.</p>\n<p>Think of us as doing &quot;neuroscience&quot; of neural networks using &quot;microscopes&quot; we build - or reverse-engineering neural networks like binary programs.</p>\n<p>More resources to learn about our work:</p>\n<ul>\n<li>Our research blog - covering advances including Monosemantic Features and Circuits</li>\n</ul>\n<ul>\n<li>An Introduction to Interpretability from our research lead, Chris Olah</li>\n</ul>\n<ul>\n<li>The Urgency of Interpretability from CEO Dario Amodei</li>\n</ul>\n<ul>\n<li>Engineering Challenges Scaling Interpretability - directly relevant to this role</li>\n</ul>\n<ul>\n<li>60 Minutes segment - Around 8:07, see a demo of tooling our team built</li>\n</ul>\n<ul>\n<li>New Yorker article - what it&#39;s like to work on one of AI&#39;s hardest open problems</li>\n</ul>\n<p>Even if you haven&#39;t worked on interpretability before, the infrastructure expertise is similar to what&#39;s needed across the lifecycle of a production language model:</p>\n<ul>\n<li>Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips</li>\n</ul>\n<ul>\n<li>Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model&#39;s internal activations mid-forward-pass - for example, adding a &quot;steering vector&quot;</li>\n</ul>\n<ul>\n<li>Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission</li>\n</ul>\n<p>The science keeps scaling - and it&#39;s now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector application</li>\n</ul>\n<ul>\n<li>Resolve scaling and efficiency bottlenecks through profiling, optimization, and close collaboration with peer infrastructure teams</li>\n</ul>\n<ul>\n<li>Design tools, abstractions, and platforms that enable researchers to rapidly experiment without hitting engineering barriers</li>\n</ul>\n<ul>\n<li>Help bring interpretability research into production safety audits - with real deadlines and high reliability expectations</li>\n</ul>\n<ul>\n<li>Work across the stack - from model internals and accelerator-level optimization to user-facing research tooling</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have 5-10+ years of experience building software</li>\n</ul>\n<ul>\n<li>Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python</li>\n</ul>\n<ul>\n<li>Are extremely curious about unfamiliar domains; can quickly learn and put that knowledge to work, e.g. diving into new layers of the stack to find bottlenecks</li>\n</ul>\n<ul>\n<li>Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions</li>\n</ul>\n<ul>\n<li>Prefer fast-moving collaborative projects to extensive solo efforts</li>\n</ul>\n<ul>\n<li>Are curious about interpretability research and its role in AI safety (though no research experience is required!)</li>\n</ul>\n<ul>\n<li>Care about the societal impacts and ethics of your work</li>\n</ul>\n<ul>\n<li>Are comfortable working closely with researchers, translating research needs into engineering solutions.</li>\n</ul>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>Optimizing the performance of large-scale distributed systems</li>\n</ul>\n<ul>\n<li>Language modeling fundamentals with transformers</li>\n</ul>\n<ul>\n<li>High Performance LLM optimization: memory management, compute efficiency, parallelism strategies, inference throughput optimization</li>\n</ul>\n<ul>\n<li>Working hands-on in a mainstream ML stack - PyTorch/CUDA on GPUs or JAX/XLA on TPUs</li>\n</ul>\n<ul>\n<li>Collaborating closely with researchers and building tooling to support research teams; or directly performed research with complex engineering challenges</li>\n</ul>\n<p>Representative Projects:</p>\n<ul>\n<li>Building Garcon, a tool that allows researchers to easily instrument LLMs to extract internal activations</li>\n</ul>\n<ul>\n<li>Designing and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them</li>\n</ul>\n<ul>\n<li>Profiling and optimizing ML training jobs, including multi-GPU parallelism and memory optimization</li>\n</ul>\n<ul>\n<li>Building a steered inference system that applies targeted interventions to model internals at scale (conceptually similar to Golden Gate Claude but for safety research)</li>\n</ul>\n<p>Role Specific Location Policy:</p>\n<ul>\n<li>This role is based in the San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.</li>\n</ul>\n<p>The annual compensation range for this role is listed below.</p>\n<p>For sales roles, the range provided is the role&#39;s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p>\n<p>Annual Salary: $315,000-$560,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_97212bdf-dd1","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4980430008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$315,000-$560,000 USD","x-skills-required":["Python","Rust","Go","Java","PyTorch","CUDA","JAX","XLA","Transformers","High Performance LLM optimization","Memory management","Compute efficiency","Parallelism strategies","Inference throughput optimization"],"x-skills-preferred":["Optimizing the performance of large-scale distributed systems","Language modeling fundamentals","Collaborating closely with researchers and building tooling to support research teams"],"datePosted":"2026-04-18T15:46:01.999Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Rust, Go, Java, PyTorch, CUDA, JAX, XLA, Transformers, High Performance LLM optimization, Memory management, Compute efficiency, Parallelism strategies, Inference throughput optimization, Optimizing the performance of large-scale distributed systems, Language modeling fundamentals, Collaborating closely with researchers and building tooling to support research teams","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":315000,"maxValue":560000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a317d234-6b0"},"title":"Data Scientist, Ads","description":"<p>We are looking for a highly motivated and experienced Data Scientist to join our growing Ads Data Science team. 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Using our privacy-preserving measurement system (Clio), we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks.</p>\n<p>The ideal candidate will be comfortable working at the intersection of empirical economics, technological change, and policy impact. They will have a strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning.</p>\n<p>Some examples of our recent work include:</p>\n<ul>\n<li>Anthropic Economic Index Report: Economic Primitives</li>\n<li>Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption</li>\n<li>Estimating AI productivity gains from Claude conversations</li>\n<li>The Anthropic Economic Index</li>\n</ul>\n<p>For this role, we&#39;re looking for candidates who can combine rigorous economic analysis with novel measurement approaches to understand AI&#39;s transformative effects on the economy.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_9327ea90-f95","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5018472008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$300,000-$405,000 USD","x-skills-required":["PhD in Economics","Strong track record of empirical research","Experience with novel data sources and economic theory","Frontier methods in causal inference and machine learning","Python, R, SQL, or similar tools for large-scale data analysis"],"x-skills-preferred":["Labor market analysis and occupational change","Task-based approaches to technological transformation","Large-scale data analysis and econometric methods","Large language models for social science research","Policy-relevant economic research"],"datePosted":"2026-04-18T15:45:19.919Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD in Economics, Strong track record of empirical research, Experience with novel data sources and economic theory, Frontier methods in causal inference and machine learning, Python, R, SQL, or similar tools for large-scale data analysis, Labor market analysis and occupational change, Task-based approaches to technological transformation, Large-scale data analysis and econometric methods, Large language models for social science research, Policy-relevant economic research","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":300000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_99450ad6-e3b"},"title":"Network Engineer - AI/HPC","description":"<p><strong>About the Role</strong></p>\n<p>We are seeking a skilled Network Engineer to join our team at xAI. As a Network Engineer, you will play a critical role in designing and operating large-scale networks for our AI and HPC systems.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and operate large-scale networks with a deep understanding of congestion control on ethernet and Infiniband</li>\n<li>Develop and optimize network configurations to ensure high performance and availability</li>\n<li>Collaborate with the team to design the next iteration of our backend and front-end networks</li>\n<li>Travel to Memphis to build capacity and participate in a team on-call rotation</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Minimum of 10 years designing and operating large-scale networks with 5 years in the ethernet AI/HPC space</li>\n<li>Deep understanding of congestion control on ethernet with Infiniband an added bonus</li>\n<li>Expertise in creating a portfolio of metrics for performance and operations to optimize the fleet for training and inference traffic</li>\n<li>Experience with Python to automate away repetitive tasks and facilitate daily job working with and analyzing large sets of data</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Opportunity to work with a highly motivated team focused on engineering excellence</li>\n<li>Collaborative and dynamic work environment</li>\n<li>Professional development opportunities</li>\n</ul>\n<p><strong>What We Offer</strong></p>\n<ul>\n<li>Competitive salary and benefits package</li>\n<li>Opportunity to work on cutting-edge AI and HPC projects</li>\n<li>Collaborative and dynamic work environment</li>\n</ul>\n<p><strong>How to Apply</strong></p>\n<p>If you are a motivated and experienced Network Engineer looking for a new challenge, please submit your application, including your resume and cover letter, to [insert contact information].</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_99450ad6-e3b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/4946691007","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["RoCEv2","NCCL","Python","Ethernet","Infiniband","AI training and inference workloads"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:45:15.340Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Memphis, TN"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"RoCEv2, NCCL, Python, Ethernet, Infiniband, AI training and inference workloads"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_29e84aed-a09"},"title":"Data Science Manager","description":"<p>We&#39;re seeking an experienced Data Science Manager to lead a team of talented data scientists and drive user growth by uncovering insights and driving strategic initiatives.</p>\n<p>As a Data Science Manager at Reddit, you will collaborate closely with cross-functional partners to help us build and improve the systems that continuously drive our user growth. You will be responsible for driving the adoption of strategic and tactical recommendations based on deep experience with Reddit products and data skills.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Serving as a thought-partner for product managers, engineering managers, and leadership to communicate and shape the roadmap and strategy for Reddit.</li>\n<li>Being proactively involved in all phases of product development, including ideation, exploratory analysis, opportunity sizing, metrics design, offline modeling, experimentation, and decision-making.</li>\n<li>Having a keen interest in the collection and quality of underlying data, including experiment design and analysis, data deep dive, ETLs, reporting dashboards, and data aggregations needed for business tracking and/or ML model development.</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>An advanced degree in a quantitative field such as statistics, mathematics, physics, economics, or operations research.</li>\n<li>10+ years of industry experience for masters holders, or 6+ years for PhD holders.</li>\n<li>Experience in driving product strategy and roadmaps through analytics, ideally for consumer technology products and marketplaces.</li>\n<li>Leadership experience as a people manager, leading a team of 5+ data scientists.</li>\n<li>Expertise in causal inference, A/B testing experimentation, metric definition and governance, and product strategy.</li>\n<li>Proficiency in SQL and Python/R for hands-on analysis.</li>\n</ul>\n<p>Benefits include comprehensive healthcare benefits, income replacement programs, 401k with employer match, global benefit programs, family planning support, gender-affirming care, mental health and coaching benefits, flexible vacation and paid volunteer time off, and generous paid parental leave.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_29e84aed-a09","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/6686373","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$217,000-$303,900 USD","x-skills-required":["data science","product strategy","analytics","SQL","Python","R","causal inference","A/B testing","metric definition","governance"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:45:14.103Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"data science, product strategy, analytics, SQL, Python, R, causal inference, A/B testing, metric definition, governance","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":217000,"maxValue":303900,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_087e2e06-4fb"},"title":"Staff Machine Learning Engineer, Ads Auction (Ads Marketplace Quality)","description":"<p>We&#39;re looking for a Staff Machine Learning Engineer to join our Ads Marketplace Quality team. As a key member of the team, you will be responsible for developing and executing a vision to improve our Ads Marketplace at Reddit. You will develop a deep understanding of our marketplace dynamics and identify areas of improvement by getting to the bottom of data, design, implement and ship algorithms to production that improve our ads marketplace efficiency.</p>\n<p>In this role, you will specialize in improving and optimizing our ads auction and pricing mechanism which will have a direct impact on upleveling the utility for both our advertiser and user values. You will also have the opportunity to work on other org-wide strategic initiatives such as supply optimization and ad relevance, where you will drive and execute on Reddit’s vision to transform Reddit into an advertising platform that shows the right ads to the right users at the right time in the right context.</p>\n<p>As a Staff Machine Learning Engineer in the Ads Marketplace Quality team, you will be an industry technical leader with domain knowledge in ads marketplace dynamics, auction and pricing, you will research, formulate, and execute on our mission to build end-to-end algorithmic solutions and deliver values to all the three-sided participants to our marketplace.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead and oversee the strategy development, quarterly planning and day-to-day execution of initiatives related to ads marketplace, auction and pricing.</li>\n<li>Proactively further our understanding of marketplace dynamics and develop algorithms to improve the efficiency and effectiveness of our ads marketplace, auction and pricing.</li>\n<li>Oversee end-to-end ML workflows,from data ingestion and feature engineering to model training, evaluation, and deployment,that optimizes the ads marketplace efficiency.</li>\n<li>Be a mentor, lead both junior and senior engineers in implementing technical designs and reviews. 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As a Senior Machine Learning Engineer for Payments, you will be the catalyst that transforms bold AI innovation into production systems that make Airbnb Payment experience feel effortless and secure.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Spearhead LLM agents, real-time anomaly detectors, and other breakthrough solutions that solve real-world problems and create product magic.</li>\n</ul>\n<ul>\n<li>Collaborate with product, engineering, ops, and data science to spot high-leverage opportunities, refine AI/ML requirements, make principled architecture choices, and measure business value with clear, data-driven metrics.</li>\n</ul>\n<ul>\n<li>Design, train, deploy, and operate large-scale AI applications for both batch and streaming workloads, ensuring low latency, high reliability, and continuous improvement via automated monitoring and retraining loops.</li>\n</ul>\n<ul>\n<li>Mentor and inspire teammates, fostering a collaborative, experimentation-driven environment where cutting-edge research meets production excellence and every engineer is empowered to push AI boundaries at Airbnb.</li>\n</ul>\n<p>Your Expertise:</p>\n<ul>\n<li>5+ years of industry experience in applied AI/ML, inclusive MS or PhD in relevant fields.</li>\n</ul>\n<ul>\n<li>Strong programming (Python/Java) and data engineering skills.</li>\n</ul>\n<ul>\n<li>Proven mastery of modern AI/LLM workflows , prompt engineering, fine-tuning (LoRA, RLHF), hallucination mitigation, safety guardrails, and rigorous online/offline testing to minimize training/inference drift and ensure reliable outcomes.</li>\n</ul>\n<ul>\n<li>Hands-on experience with at least three of the following: PyTorch/TensorFlow, scalable inference stacks, vector search, orchestration/MLOps platforms (Kubeflow, Airflow), large-scale data streaming &amp; processing (Spark, Ray, Kafka).</li>\n</ul>\n<ul>\n<li>Demonstrated success designing, deploying, and monitoring production AI systems , e.g., personalization engines, generative content services , complete with drift/cost/latency monitoring, automated retraining triggers, and cross-functional collaboration that translates ambiguous business needs into measurable AI impact.</li>\n</ul>\n<ul>\n<li>Prior knowledge of AI/ML applications in the Payments domain is highly desirable.</li>\n</ul>\n<p>Our Commitment To Inclusion &amp; Belonging:</p>\n<p>Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively led people, and to develop the best products, services, and solutions.</p>\n<p>How We&#39;ll Take Care of You:</p>\n<p>Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.</p>\n<p>Pay Range: $191,000-$223,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_5aacaad3-05b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Airbnb","sameAs":"https://www.airbnb.com/","logo":"https://logos.yubhub.co/airbnb.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/airbnb/jobs/7755758","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$191,000-$223,000 USD","x-skills-required":["Python","Java","PyTorch","TensorFlow","scalable inference stacks","vector search","orchestration/MLOps platforms","large-scale data streaming & processing"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:43:02.517Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote-USA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Java, PyTorch, TensorFlow, scalable inference stacks, vector search, orchestration/MLOps platforms, large-scale data streaming & processing","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":191000,"maxValue":223000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_094a1977-5fc"},"title":"Member of Technical Staff - Inference","description":"<p>We&#39;re looking for a Member of Technical Staff - Inference to join our team.</p>\n<p>Our mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence.</p>\n<p>As a Member of Technical Staff - Inference, you will be responsible for optimising the latency and throughput of model inference, building reliable and performant production serving systems to serve billions of users, accelerating research on scaling test-time compute and rollout in reinforcement learning training, and model-hardware co-design for next-generation architectures.</p>\n<p>To be successful in this role, you will need to have worked on system optimisations for model serving, such as batching, caching, load balancing, and parallelism, worked on low-level optimisations for inference, such as GPU kernels and code generation, worked on algorithmic optimisations for inference, such as quantisation, distillation, and speculative decoding, and low-precision numerics, worked on large-scale inference engines or reinforcement learning frameworks, worked on large-scale, high-concurrent production serving, and worked on testing, benchmarking, and reliability of inference services.</p>\n<p>The base salary for this role is $180,000 - $440,000 USD, and we offer a comprehensive total rewards package, including equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short &amp; long-term disability insurance, life insurance, and various other discounts and perks.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_094a1977-5fc","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/4533894007","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$180,000 - $440,000 USD","x-skills-required":["System optimisations for model serving","Low-level optimisations for inference","Algorithmic optimisations for inference","Large-scale inference engines or reinforcement learning frameworks","Large-scale, high-concurrent production serving"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:42:20.456Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"System optimisations for model serving, Low-level optimisations for inference, Algorithmic optimisations for inference, Large-scale inference engines or reinforcement learning frameworks, Large-scale, high-concurrent production serving","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":440000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_844314bc-a01"},"title":"Senior Data Scientist - Inference, Global Markets","description":"<p>Join the Global Markets team at Airbnb, focusing on evolving guest and host experiences for global markets to accelerate international growth. As a Senior Data Scientist, you will partner closely with product managers, designers, engineers, and operations across regions to build and optimize products that resonate with Airbnb guests and hosts worldwide.</p>\n<p>A typical day involves working closely with cross-functional stakeholders to define product scopes, evaluating impact, and setting roadmap priorities. You will architect and implement rigorous measurement plans, using A/B tests and quasi-experimental methods to assess product success and inform strategic bets. Additionally, you will interpret unexpected outcomes, identify bias in experiments, and drive solutions to ensure measurement quality.</p>\n<p>Your expertise in causal inference and experimentation will be crucial in developing scalable frameworks, models, and systems that enable product features to be more refined and tailored to the needs of local markets. You will actively present data findings and ideas to stakeholders at different levels, turning proposals into actions and tangible results for the business and our customers.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Working closely with cross-functional stakeholders to define product scopes, evaluate impact, and set roadmap priorities</li>\n<li>Architecting and implementing rigorous measurement plans, using A/B tests and quasi-experimental methods to assess product success and inform strategic bets</li>\n<li>Interpreting unexpected outcomes, identifying bias in experiments, and driving solutions to ensure measurement quality</li>\n<li>Conducting in-depth research into customer behaviors and preferences across markets to unlock opportunities for international business growth</li>\n<li>Developing scalable frameworks, models, and systems that enable product features to be more refined and tailored to the needs of local markets</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>5+ years of industry experience in a fast-paced tech environment with a BS/Master&#39;s in a technical field related to mathematics, computer science, statistics, economics, machine learning, or 2+ years of relevant experience and a PhD in similar fields</li>\n<li>Strong knowledge of causal inference and experimentation</li>\n<li>Expertise in SQL, Python, or R</li>\n<li>Ability to solve business problems using appropriate methods and models</li>\n<li>Strong stakeholder communication skills and the ability to translate complex analyses into compelling narratives and business actions</li>\n</ul>\n<p>This position is remote eligible, with occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_844314bc-a01","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Airbnb","sameAs":"https://www.airbnb.com/","logo":"https://logos.yubhub.co/airbnb.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/airbnb/jobs/7446449","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["causal inference","experimentation","SQL","Python","R","statistics","machine learning"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:42:00.754Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"China"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"causal inference, experimentation, SQL, Python, R, statistics, machine learning"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3ac0b2f4-6c9"},"title":"Member of Technical Staff - Imagine Product","description":"<p><strong>About the Role</strong></p>\n<p>The Imagine Product team is redefining AI-driven media experiences for Grok users worldwide. You&#39;ll build and scale robust, high-performance systems that power immersive, multi-modal media interactions,leveraging cutting-edge AI to enable seamless generation, processing, and delivery of images, video, audio, and beyond.</p>\n<p>Your work will drive engaging, real-time user experiences that captivate and delight millions, turning advanced multimodal models into production-grade features. If you&#39;re a driven problem-solver passionate about AI, media technologies, and creating scalable solutions that shape the future of consumer AI, this is your opportunity to make a lasting impact.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and implement scalable systems to support Grok&#39;s AI-driven media experiences, ensuring high performance, reliability, and low-latency at global scale.</li>\n<li>Architect robust infrastructure for real-time multi-modal interactions, including handling generation requests, media processing, and seamless integration with frontend and model serving layers.</li>\n<li>Build and optimise large-scale data pipelines to ingest, process, and analyse multi-modal data (images, video, audio), fueling continuous improvement and personalisation of Grok&#39;s media capabilities.</li>\n<li>Collaborate closely with frontend engineers, AI researchers, and product teams to deliver captivating, media-rich features and end-to-end user experiences.</li>\n<li>Own full-cycle development of solutions: from system design and prototyping to deployment, monitoring, observability, and iterative refinement.</li>\n<li>Deliver production-ready, maintainable code that powers features reaching hundreds of millions of users.</li>\n</ul>\n<p><strong>Basic Qualifications</strong></p>\n<ul>\n<li>Proficiency in Python or Rust, with a strong track record of writing clean, efficient, maintainable, and scalable code.</li>\n<li>Experience designing and building systems for consumer-facing products, with emphasis on performance, reliability, and handling high-throughput workloads.</li>\n<li>Hands-on expertise in large-scale data infrastructure and pipelines, particularly for multi-modal or media-heavy AI applications.</li>\n<li>Proven ability to deliver robust, production-grade solutions to millions of users while maintaining high standards of quality and uptime.</li>\n<li>Strong problem-solving skills and a passion for turning innovative ideas into high-impact, scalable realities.</li>\n<li>Deep enthusiasm for AI and media technologies, with a commitment to building user-focused products that inspire and engage.</li>\n</ul>\n<p><strong>Preferred Skills and Experience</strong></p>\n<ul>\n<li>Experience with real-time systems, inference serving, or multi-modal data processing at scale.</li>\n<li>Familiarity with distributed systems, containerisation (e.g., Kubernetes), observability tools, or performance tuning for AI workloads.</li>\n<li>Background in AI-driven consumer products or media generation technologies.</li>\n<li>Track record collaborating across engineering, research, and product teams to ship delightful features quickly.</li>\n</ul>\n<p><strong>Compensation and Benefits</strong></p>\n<p>$180,000 - $440,000 USD</p>\n<p>Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short &amp; long-term disability insurance, life insurance, and various other discounts and perks.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_3ac0b2f4-6c9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://xAI.com","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5052027007","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$180,000 - $440,000 USD","x-skills-required":["Python","Rust","clean, efficient, maintainable, and scalable code","large-scale data infrastructure and pipelines","multi-modal or media-heavy AI applications","production-grade solutions","quality and uptime"],"x-skills-preferred":["real-time systems","inference serving","multi-modal data processing at scale","distributed systems","containerisation","observability tools","performance tuning for AI workloads","AI-driven consumer products","media generation technologies"],"datePosted":"2026-04-18T15:41:51.975Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Rust, clean, efficient, maintainable, and scalable code, large-scale data infrastructure and pipelines, multi-modal or media-heavy AI applications, production-grade solutions, quality and uptime, real-time systems, inference serving, multi-modal data processing at scale, distributed systems, containerisation, observability tools, performance tuning for AI workloads, AI-driven consumer products, media generation technologies","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":440000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_40d7aa0b-d9c"},"title":"Principal Data Scientist","description":"<p>We are looking for a Principal Data Scientist to serve as the statistical voice of the Data Science organization. This person will make Databricks smarter and more data-driven at the highest levels of leadership , translating the full power of data science into clear, actionable narratives for our CEO, C-suite, and Board of Directors.</p>\n<p>Our vision is simple: data drives every Databricks decision and action. To get there, we need a world-class statistician and communicator , someone who can bridge the gap between deep analytical rigor and executive decision-making. This is a pure IC role with company-wide influence: no direct reports, maximum leverage.</p>\n<p>As a Principal Data Scientist, you will:</p>\n<ul>\n<li>Translate complex data science findings into clear, actionable narratives for the CEO, C-suite, and Board of Directors , ensuring data science insights directly inform the company&#39;s most critical decisions.</li>\n<li>Serve as the company&#39;s chief statistical voice and the final quality backstop for analytical rigor in high-stakes executive decisions. Advance the state-of-the-art in how Databricks applies statistical methods to business problems.</li>\n<li>Raise the communication bar across the entire Data Science organization by setting standards, coaching teams, and co-authoring key executive-facing deliverables. Make every DS team better at telling their story.</li>\n<li>Produce deep strategic analyses on revenue, platform health, operational efficiency, and competitive positioning , the kind of synthesized, judgment-rich insight that AI cannot autonomously create.</li>\n<li>Partner with engineering VPs, product leaders, and executive staff to embed a data-driven decision-making culture across the company. Be the trusted analytical advisor in rooms where critical decisions are made.</li>\n<li>Represent Databricks externally as a data science thought leader at industry conferences, in publications, and in the broader statistical community. Build an external identity that attracts world-class talent.</li>\n<li>Define and evolve company-wide scientific methodologies , experimentation frameworks, forecasting systems, causal inference approaches , to match and push industry state-of-the-art.</li>\n</ul>\n<p>To be successful in this role, you will need:</p>\n<ul>\n<li>15+ years of experience in data science, statistics, or quantitative research spanning industry and/or academia.</li>\n<li>Proven track record of presenting statistical and data science concepts to C-suite and Board-level audiences, with measurable impact on executive decision-making.</li>\n<li>Broad expertise across data science disciplines: experimentation, causal inference, forecasting, optimization, and machine learning.</li>\n<li>Exceptional written and verbal communication , the ability to make complex statistical concepts intuitive and compelling for non-technical executives.</li>\n<li>Track record of upleveling teams: setting analytical standards, mentoring senior data scientists, and improving org-wide output quality.</li>\n<li>Experience at the intersection of statistics and large-scale technology or data platforms.</li>\n<li>Ph.D. in Statistics, Mathematics, Computer Science, or a related quantitative field.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_40d7aa0b-d9c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8456277002","x-work-arrangement":"remote","x-experience-level":"executive","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["data science","statistics","quantitative research","experimentation","causal inference","forecasting","optimization","machine learning","written communication","verbal communication"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:40:41.951Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - California; San Francisco, California; Seattle, Washington"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"data science, statistics, quantitative research, experimentation, causal inference, forecasting, optimization, machine learning, written communication, verbal communication"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_63af8568-789"},"title":"Engineering Manager, Inference Routing and Performance","description":"<p><strong>About the role\\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what&#39;s already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\\n\\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn&#39;t have been shed.\\n\\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic&#39;s inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\\n\\nAs Anthropic moves from &quot;many independent inference replicas&quot; toward &quot;a single warehouse-scale computer running a coordinated program,&quot; Dystro is the coordination layer. This is a deeply technical team.\\n\\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\\n\\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\\n\\nThe EM for this team doesn&#39;t need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\\n\\nYou&#39;ll inherit a strong team of distributed-systems engineers, and you&#39;ll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\\n\\nThe job is holding both.\\n\\n## Representative work:\\nThings the Inference Routing EM actually spends time on:\\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they&#39;d be additive to a team that already goes deep\\n\\n## What you&#39;ll do:\\nDrive system-level performance\\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\\n- Build the team&#39;s habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\\n\\nDeliver reliably and operate cleanly\\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\\n- Run the team&#39;s operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You&#39;ll make sure commitments are realistic, dependencies are understood, and nobody is surprised\\n\\nBuild and grow the team\\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you&#39;ll help make that collaboration pattern productive\\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\\n\\n## You may be a good fit if you:\\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\\n- Are curious about machine learning systems. You don&#39;t need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\\n\\nStrong candidates may also have:\\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\\n- Led teams at supercomputing or hyperscaler infrastructure scale\\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\\n\\nThe annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\nAnnual Salary: $405,000-$485,000 USD</strong></p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_63af8568-789","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5155391008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$405,000-$485,000 USD","x-skills-required":["engineering management","deep systems background","load balancing","scheduling","cache-coherent distributed state","high-performance networking"],"x-skills-preferred":["LLM inference serving","cluster schedulers","load balancers","service meshes","coordination planes","heterogeneous accelerator fleets","GPU/TPU/Trainium","GPU/accelerator programming","ML framework internals","OS-level performance debugging"],"datePosted":"2026-04-18T15:37:38.038Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"engineering management, deep systems background, load balancing, scheduling, cache-coherent distributed state, high-performance networking, LLM inference serving, cluster schedulers, load balancers, service meshes, coordination planes, heterogeneous accelerator fleets, GPU/TPU/Trainium, GPU/accelerator programming, ML framework internals, OS-level performance debugging","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":405000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_0a872d93-7f6"},"title":"Engineering Manager, Cloud Inference AWS","description":"<p>We are seeking an experienced Engineering Manager to lead the Cloud Inference team for AWS. You will lead your team to scale and optimize Claude to serve the massive audiences of developers and enterprise companies using AWS.</p>\n<p>As an Engineering Manager, you will own the end-to-end product of Claude on AWS, including API, load balancing, inference, capacity and operations. Your team will ensure our LLMs meet rigorous performance, safety and security standards and enhance our core infrastructure for packaging, testing, and deploying inference technology across the globe.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Set technical strategy and oversee development of Claude on AWS across all layers of the technical stack.</li>\n<li>Collaborate across teams and companies to deeply understand product, infrastructure, operations and capacity needs, identifying potential solutions to support frontier LLM serving</li>\n<li>Work closely with cross-functional stakeholders across companies to align on goals and drive outcomes</li>\n<li>Create clarity for the team and stakeholders in an ambiguous and evolving environment</li>\n<li>Take an inclusive approach to hiring and coaching top technical talent, and support a high performing team</li>\n<li>Design and run processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>10+ years of experience in high-scale, high-reliability software development, particularly infrastructure or capacity management</li>\n<li>5+ years of engineering management experience</li>\n<li>Experience recruiting, scaling, and retaining engineering talent in a high growth environment</li>\n<li>Have experience scaling products, resources and operations to accommodate rapid growth</li>\n<li>Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development</li>\n<li>Excel at building strong relationships and strategy with stakeholders across engineering, product, finance, and sales</li>\n<li>Have experience working with external partners to align goals and deliver impact</li>\n<li>Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space</li>\n<li>Have excellent written and verbal communication skills</li>\n<li>Demonstrated success building a culture of belonging and engineering excellence</li>\n<li>Are motivated by developing AI responsibly and safely</li>\n<li>Are willing and able to travel frequently between Seattle and the SF Bay Area</li>\n</ul>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL</li>\n<li>Experience as a Product Manager</li>\n<li>Experience with deployment and capacity management automation</li>\n<li>Security and privacy best practice expertise</li>\n</ul>\n<p>Annual compensation range for this role is $405,000-$485,000 USD.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_0a872d93-7f6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5141377008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$405,000-$485,000 USD","x-skills-required":["Cloud Inference","AWS","Machine Learning","Infrastructure Management","Capacity Planning","Security and Privacy","Leadership","Communication","Collaboration"],"x-skills-preferred":["GPU","TPU","Trainium","NCCL","Product Management","Deployment Automation","Security Best Practices"],"datePosted":"2026-04-18T15:37:12.539Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Cloud Inference, AWS, Machine Learning, Infrastructure Management, Capacity Planning, Security and Privacy, Leadership, Communication, Collaboration, GPU, TPU, Trainium, NCCL, Product Management, Deployment Automation, Security Best Practices","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":405000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_55e8fefe-652"},"title":"Senior Perception & Autonomy Engineer","description":"<p>We are seeking a Senior Perception &amp; Autonomy Engineer to play a pivotal role in designing, developing, and implementing perception systems for our autonomous surface vessels.</p>\n<p>Our team is focused on making boats go and perform tasks with no human involvement. 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As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3).</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_55e8fefe-652","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Saronic Technologies","sameAs":"https://www.saronictechnologies.com/","logo":"https://logos.yubhub.co/saronictechnologies.com.png"},"x-apply-url":"https://jobs.lever.co/saronic/d770926d-1d32-40d2-a43d-7fc4c6fd9350","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["strong programming fundamentals","extensive programming experience","computing fundamentals","familiarity with deep learning frameworks","proficiency in Rust"],"x-skills-preferred":["experience with maritime or autonomous vehicle projects","experience with signals processing or sensor fusion","experience with low latency inference and tracking pipelines","experience with path planning algorithms","experience training and deploying multi modal models"],"datePosted":"2026-04-17T12:57:31.628Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"strong programming fundamentals, extensive programming experience, computing fundamentals, familiarity with deep learning frameworks, proficiency in Rust, experience with maritime or autonomous vehicle projects, experience with signals processing or sensor fusion, experience with low latency inference and tracking pipelines, experience with path planning algorithms, experience training and deploying multi modal models"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_edaaa5b1-6da"},"title":"Perception Engineer","description":"<p>We are seeking a Perception Engineer to play a pivotal role in designing, developing, and implementing perception systems for our autonomous surface vessels.</p>\n<p>Our team is focused on making boats go and perform tasks with no human involvement. 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As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3).</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_edaaa5b1-6da","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Saronic Technologies","sameAs":"https://www.saronictechnologies.com/","logo":"https://logos.yubhub.co/saronictechnologies.com.png"},"x-apply-url":"https://jobs.lever.co/saronic/30af5320-d158-4127-969f-de7ee92504ce","x-work-arrangement":"onsite","x-experience-level":"entry|senior|staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Strong programming fundamentals","Extensive programming experience and demonstrated ability to work on large systems","Computing Fundamentals","Understanding of basic computer architecture","Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)","Proficiency in Rust","Experience with maritime or autonomous vehicle projects","Experience with signals processing or sensor fusion","Experience with low latency inference and tracking pipelines","Experience with path planning algorithms","Experience training and deploying multi modal models","Experience with various sensors including radar, cameras, and lidar","Experience developing and optimizing deployed ML systems"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:56:56.687Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Strong programming fundamentals, Extensive programming experience and demonstrated ability to work on large systems, Computing Fundamentals, Understanding of basic computer architecture, Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), Proficiency in Rust, Experience with maritime or autonomous vehicle projects, Experience with signals processing or sensor fusion, Experience with low latency inference and tracking pipelines, Experience with path planning algorithms, Experience training and deploying multi modal models, Experience with various sensors including radar, cameras, and lidar, Experience developing and optimizing deployed ML systems"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_586b9fef-509"},"title":"Senior Software Engineer - Network Enablement (Applied ML)","description":"<p>We believe that the way people interact with their finances will drastically improve in the next few years. We&#39;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products.</p>\n<p>On this team, you will build and operate the ML infrastructure and product services that enable trust and intelligence across Plaid&#39;s network. You&#39;ll own feature engineering, offline training and batch scoring, online feature serving, and real-time inference so model outputs directly power partner-facing fraud &amp; trust products and bank intelligence features.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows).</li>\n<li>Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact).</li>\n<li>Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses.</li>\n<li>Build and operate offline training pipelines and production batch scoring for bank intelligence products.</li>\n<li>Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring.</li>\n<li>Implement model CI/CD, model/version registry, and safe rollout/rollback strategies.</li>\n<li>Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs.</li>\n<li>Ensure offline and online parity, data lineage, and automated validation / data contracts to reduce regressions.</li>\n<li>Optimize inference performance and cost for real-time scoring (batching, caching, runtime selection).</li>\n<li>Ensure fairness, explainability and PII-aware handling for partner-facing ML features; maintain auditability for compliance.</li>\n<li>Partner with platform and cross-functional teams to scale the ML/data foundation (graph features, sequence embeddings, unified pipelines).</li>\n<li>Mentor engineers and document team standards for ML productization and operations.</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<ul>\n<li>Must-haves:</li>\n<li>Strong software engineering skills including systems design, APIs, and building reliable backend services (Go or Python preferred).</li>\n<li>Production experience with batch and streaming data pipelines and orchestration tools such as Airflow or Spark.</li>\n<li>Experience building or operating real-time scoring and online feature-serving systems, including feature stores and low-latency model inference.</li>\n<li>Experience integrating model outputs into product flows (APIs, feature flags) and measuring impact through experiments and product metrics.</li>\n<li>Experience with model lifecycle and operations: model registries, CI/CD for models, reproducible training, offline &amp; online parity, monitoring and incident response.</li>\n<li>Nice to have:</li>\n<li>Experience in fraud, risk, or marketing intelligence domains.</li>\n<li>Experience with feature-store products (Tecton / Chronon / Feast / internal) and unified pipelines.</li>\n<li>Experience with graph frameworks, graph feature engineering, or sequence embeddings.</li>\n<li>Experience optimizing inference at scale (Triton/ONNX/quantization, batching, caching).</li>\n</ul>\n<p><strong>Additional Information</strong></p>\n<p>Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_586b9fef-509","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Plaid","sameAs":"https://plaid.com/","logo":"https://logos.yubhub.co/plaid.com.png"},"x-apply-url":"https://jobs.lever.co/plaid/43b1374d-5c5e-4b63-b710-a95e3cb76bbe","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$190,800-$286,800 per year","x-skills-required":["software engineering","systems design","APIs","backend services","Go","Python","batch and streaming data pipelines","orchestration tools","Airflow","Spark","real-time scoring","online feature-serving systems","feature stores","low-latency model inference","model outputs","product flows","experiments","product metrics","model lifecycle","operations","model registries","CI/CD","reproducible training","offline & online parity","monitoring","incident response"],"x-skills-preferred":["fraud","risk","marketing intelligence","feature-store products","unified pipelines","graph frameworks","graph feature engineering","sequence embeddings","inference at scale","Triton","ONNX","quantization","batching","caching"],"datePosted":"2026-04-17T12:51:26.228Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, systems design, APIs, backend services, Go, Python, batch and streaming data pipelines, orchestration tools, Airflow, Spark, real-time scoring, online feature-serving systems, feature stores, low-latency model inference, model outputs, product flows, experiments, product metrics, model lifecycle, operations, model registries, CI/CD, reproducible training, offline & online parity, monitoring, incident response, fraud, risk, marketing intelligence, feature-store products, unified pipelines, graph frameworks, graph feature engineering, sequence embeddings, inference at scale, Triton, ONNX, quantization, batching, caching","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":190800,"maxValue":286800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6e92655b-cbb"},"title":"Senior Data Scientist - Banking","description":"<p>We&#39;re looking for a full-stack Data Scientist to support our Cards &amp; Credit roadmap, partnering closely with Product, Engineering, Design, Underwriting, and Operations to shape how our card and credit products evolve and scale.</p>\n<p>In this role, you&#39;ll apply strong analytical judgment and product intuition to help us understand customer behaviour, evaluate trade-offs, and make smart investment decisions across the cards and lending lifecycles , from eligibility and activation to spend, retention, incentives, and credit performance. You&#39;ll help build a data-informed culture across Mercury so teams can move quickly, measure what matters, and invest intelligently.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Bringing impeccable communication and complete ownership , independently identifying opportunities, developing strong points of view, and influencing executives, Cards &amp; Credit leaders, and cross-functional partners through clear, concise, and persuasive storytelling.</li>\n<li>Developing a nuanced understanding of cardholder behaviour and economics, helping teams reason about trade-offs between growth, engagement, risk, and unit economics.</li>\n<li>Defining, owning, and analysing metrics that inform both tactical decisions and long-term strategy across the cards and credit lifecycle (e.g., eligibility, activation, spend, utilisation, rewards, retention, loss signals).</li>\n<li>Designing and evaluating experiments using rigorous statistical approaches, including A/B testing, cohort analysis, causal inference techniques, and trend analysis.</li>\n<li>Building and improving data pipelines and tools to streamline data collection, processing, and analysis workflows, ensuring the integrity, reliability, and security of data assets.</li>\n<li>Building and deploying predictive models to forecast key outcomes, inform product treatments, and deepen understanding of causal drivers.</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>7+ years of experience working with large datasets to drive product or business impact in data science or analytics roles.</li>\n<li>Fluency in SQL and comfort with Python.</li>\n<li>Strong judgment in defining and analysing product metrics, running experiments, and translating ambiguous questions into structured analyses.</li>\n<li>Exceptional proactivity and independence , identifying opportunities, forming strong points of view, and making your case to stakeholders.</li>\n<li>Experience with ETL processes and modern data modelling (e.g., dbt, dimensional models, Airflow), with a solid understanding of how data is produced and consumed.</li>\n<li>Experience in analytical approaches ranging from behavioural modelling to experimentation to optimisation , and, importantly, know when simpler approaches are the right answer.</li>\n<li>Apply AI tools to accelerate analytical and business workflows, improving scalability, decision quality, and reducing manual or repetitive work across teams.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience working on cards or credit products, with familiarity in card economics and lifecycle concepts (e.g., spend behaviour, interchange, rewards and incentives, utilisation, credit limits, retention).</li>\n<li>Experience developing quantitative pricing models or engines (e.g., dynamic pricing, incentive optimisation, or marketplace pricing systems).</li>\n<li>Experience applying optimisation techniques to resource allocation or decision systems (e.g., customer operations, capacity planning, or policy optimisation).</li>\n<li>Experience building or supporting credit models, including probability of default modelling, cashflow modelling, or dynamic credit limit setting.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_6e92655b-cbb","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mercury","sameAs":"https://www.mercury.com/","logo":"https://logos.yubhub.co/mercury.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/mercury/jobs/5799320004","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$200,700 - 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We strive to continually improve our working environment, and provide you with excellent facilities such as healthy food, an on-site gym, faith rooms, terraces etc.</p>\n<p>We are also open to relocating candidates and offer a bespoke service and immigration support to make it as easy as possible (depending on eligibility).</p>\n<p>The US base salary range for this full-time position is between $136,000 - $245,000 + bonus + equity + benefits. 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As a trusted technical advisor to our CAE developers and customers, you will be responsible for embedding NVIDIA software into developers&#39; architectures and workflows.</p>\n<p><strong>What you&#39;ll be doing:</strong></p>\n<ul>\n<li>Support Business Development and Sales teams as part of a team of 4, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive our developers&#39; ecosystem success.</li>\n<li>Work directly with developers and customers in a customer-facing setting.</li>\n<li>Support developers in adopting NVIDIA libraries and software frameworks as the foundation for modern AI and data platforms.</li>\n<li>Analyze application architectures and find opportunities for acceleration.</li>\n<li>Provide feedback and collaborate with engineering, product, and research teams.</li>\n<li>Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms.</li>\n</ul>\n<p><strong>What we need to see:</strong></p>\n<ul>\n<li>A MS/PhD degree in Machine Learning, Computational Science, Physics, or a related technical field.</li>\n<li>Minimum of 5 years of technical experience in Physics-Machine Learning.</li>\n<li>Experience in engineering simulations (e.g. fluid dynamics, atmospheric science, Computer-Aided Engineering technologies).</li>\n<li>Familiarity with accelerated computing platforms and GPU-based distributed systems.</li>\n<li>Experience in algorithm programming using languages like Python and C/C++.</li>\n<li>Development experience using major AI frameworks (e.g., PyTorch, Tensorflow, and similar tools).</li>\n<li>Familiarity with containers, numerical libraries, modular software design, version control, GitHub.</li>\n<li>Experience designing, prototyping, and building complex AI/ML-based solutions for customers.</li>\n<li>Able to reason across components such as data pipelines, models, compute, networking, and orchestration.</li>\n<li>Solid written and oral 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business opportunities for NVIDIA products and solutions for ML/DL and other software solutions</li>\n<li>Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.</li>\n<li>Conduct regular technical customer meetings for project/product roadmap, feature discussions, and intro to new technologies. 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We&#39;re combining first-principles thinking with cutting-edge technology to build a radically better energy system.</p>\n<p>As the Head of Engineering (AI) at Fuse Energy, you will lead the development and integration of AI across our platform—from intelligent forecasting models and optimization algorithms to personalized customer experiences and internal automation.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Own the AI engineering roadmap and lead the development of AI-first features</li>\n<li>Productionize ML models, ensuring scalability, performance, and observability</li>\n<li>Design the infrastructure for deploying and maintaining ML systems in production (e.g., MLOps, CI/CD for ML, model versioning)</li>\n<li>Build systems that integrate AI into key parts of our stack, such as:</li>\n<li>Forecasting customer demand and renewable generation</li>\n<li>Dynamic pricing and energy trading algorithms</li>\n<li>Intelligent alerts and personalized customer features</li>\n<li>Work closely with product and engineering leadership to identify high-impact AI opportunities</li>\n<li>Build and lead a high-performing team of AI engineers</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Strong software engineering background with 5+ years of experience, including at least 2 years leading AI/ML engineering teams</li>\n<li>Deep experience deploying ML models into production environments</li>\n<li>Proficiency in designing scalable data pipelines and real-time inference systems</li>\n<li>Understanding of modern ML tooling and frameworks (e.g., PyTorch, TensorFlow, MLflow, AWS SageMaker)</li>\n<li>Strong cross-functional collaboration skills, particularly with data science and product teams</li>\n<li>Clear communication and an ability to prioritize for both experimentation and reliability</li>\n</ul>\n<p><strong>Bonus</strong></p>\n<ul>\n<li>Familiarity with optimization, time series modeling, or forecasting</li>\n<li>Experience with large language models (LLMs), RAG, or generative AI in production</li>\n<li>Background in MLOps or AI infrastructure at scale</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive salary and an equity sign-on bonus</li>\n<li>Biannual bonus scheme</li>\n<li>Fully expensed tech to match your needs</li>\n<li>Paid annual leave</li>\n<li>Breakfast and dinner allowance for office-based employees</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_6186a306-374","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Fuse Energy","sameAs":"https://jobs.workable.com","logo":"https://logos.yubhub.co/view.com.png"},"x-apply-url":"https://jobs.workable.com/view/xz8u7PJwq8wtrGKdHBNFmd/hybrid-head-of-engineering-(ai)-in-dubai-at-fuse-energy","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Strong software engineering background","Deep experience deploying ML models into production environments","Proficiency in designing scalable data pipelines and real-time inference systems","Understanding of modern ML tooling and frameworks","Strong cross-functional collaboration skills"],"x-skills-preferred":["Familiarity with optimization, time series modeling, or forecasting","Experience with large language models (LLMs), RAG, or generative AI in production","Background in MLOps or AI infrastructure at scale"],"datePosted":"2026-03-09T16:57:47.100Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dubai"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Strong software engineering background, Deep experience deploying ML models into production environments, Proficiency in designing scalable data pipelines and real-time inference systems, Understanding of modern ML tooling and frameworks, Strong cross-functional collaboration skills, Familiarity with optimization, time series modeling, or forecasting, Experience with large language models (LLMs), RAG, or generative AI in production, Background in MLOps or AI infrastructure at scale"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1d0184f1-be6"},"title":"Security Engineer","description":"<p><strong>About the Role</strong></p>\n<p>We&#39;re hiring our first Security Engineer to own the process of safeguarding our systems, infrastructure, applications, and data. As the first security hire, you will build out our security operations and vulnerability management process for our AI gateway platform. You&#39;ll implement programs, run tooling, ship security fixes, and drive remediation across our stack. You’ll be responsible for all aspects of ensuring the security of our platform and users. This isn&#39;t a compliance paperwork role; it&#39;s a hands-on security position with direct impact on how we protect millions of API requests daily. You&#39;ll work closely with engineering and senior leadership to ship security improvements that actually matter.</p>\n<p><strong>What You&#39;ll Do</strong></p>\n<ul>\n<li>Deploy and operate vulnerability scanning across our cloud infrastructure. Triage findings and drive remediation with engineering teams.</li>\n</ul>\n<ul>\n<li>Lead security assessments for internal and customer security needs (e.g. SOC 2 Type II, ISO 27001, HIPAA audits).</li>\n</ul>\n<ul>\n<li>Maintain vulnerability and remediation documentation for auditors.</li>\n</ul>\n<ul>\n<li>Act as a liaison between product, engineering, compliance, and GTM to guide and prioritize the right security investments.</li>\n</ul>\n<ul>\n<li>Perform penetration tests, tabletop exercises, DR testing, and incident response.</li>\n</ul>\n<ul>\n<li>Manage endpoint security tooling as we scale; conduct audit log reviews and maintain visibility across our stack.</li>\n</ul>\n<p><strong>About You</strong></p>\n<ul>\n<li>3-5+ years in security engineering or operations.</li>\n</ul>\n<ul>\n<li>Deep knowledge of cloud security and expertise in operating in a cloud-hosted environment.</li>\n</ul>\n<ul>\n<li>Comfortable in compliance-heavy environments (SOC 2, ISO 27001, HIPAA).</li>\n</ul>\n<ul>\n<li>Strong experience with SIEM platforms (Splunk, Elastic, Panther) and vulnerability scanners (e.g. Qualys, Tenable, Rapid7).</li>\n</ul>\n<ul>\n<li>AI-forward with hands-on experience adopting, leveraging, and integrating AI tools.</li>\n</ul>\n<ul>\n<li>Startup mindset; you thrive building programs from the ground up and not just inheriting existing playbooks.</li>\n</ul>\n<ul>\n<li>Pragmatic and business-oriented, able to balance security rigor and business speed.</li>\n</ul>\n<ul>\n<li>Ability to communicate risk and technical ideas clearly to both technical and non-technical audiences.</li>\n</ul>\n<p><strong>Bonus Points</strong></p>\n<ul>\n<li>Experience with AI/ML infrastructure or inference platforms.</li>\n</ul>\n<ul>\n<li>Automation and scripting with Python.</li>\n</ul>\n<ul>\n<li>Healthcare data handling or BAA compliance experience.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_1d0184f1-be6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenRouter","sameAs":"https://jobs.ashbyhq.com","logo":"https://logos.yubhub.co/openrouter.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openrouter/188d9898-d4e0-4895-8203-86063af0ee41","x-work-arrangement":"Remote","x-experience-level":"mid","x-job-type":"Full time","x-salary-range":null,"x-skills-required":["cloud security","vulnerability scanning","SIEM platforms","vulnerability scanners","AI tools","endpoint security tooling"],"x-skills-preferred":["AI/ML infrastructure","inference platforms","Python scripting","healthcare data handling","BAA compliance"],"datePosted":"2026-03-09T09:48:01.907Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote (US)"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"cloud security, vulnerability scanning, SIEM platforms, vulnerability scanners, AI tools, endpoint security tooling, AI/ML infrastructure, inference platforms, Python scripting, healthcare data handling, BAA compliance"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a51375e8-30e"},"title":"Member of Technical Staff, Software Co-Design AI HPC Systems","description":"<p>Our team&#39;s mission is to architect, co-design, and productionize next-generation AI systems at datacenter scale. We operate at the intersection of models, systems software, networking, storage, and AI hardware, optimizing end-to-end performance, efficiency, reliability, and cost. Our work spans today&#39;s frontier AI workloads and directly shapes the next generation of accelerators, system architectures, and large-scale AI platforms. We pursue this mission through deep hardware–software co-design, combining rigorous systems thinking with hands-on engineering. The team invests heavily in understanding real production workloads large-scale training, inference, and emerging multimodal models and translating those insights into concrete improvements across the stack: from kernels, runtimes, and distributed systems, all the way down to silicon-level trade-offs and datacenter-scale architectures. This role sits at the boundary between exploration and production. You will work closely with internal infrastructure, hardware, compiler, and product teams, as well as external partners across the hardware and systems ecosystem. Our operating model emphasizes rapid ideation and prototyping, followed by disciplined execution to drive high-leverage ideas into production systems that operate at massive scale. In addition to delivering real-world impact on large-scale AI platforms, the team actively contributes to the broader research and engineering community. Our work aligns closely with leading communities in ML systems, distributed systems, computer architecture, and high-performance computing, and we regularly publish, prototype, and open-source impactful technologies where appropriate.</p>\n<p>About the Team</p>\n<p>We build foundational AI infrastructure that enables large-scale training and inference across diverse workloads and rapidly evolving hardware generations. Our work directly shapes how AI systems are designed, deployed, and scaled today and into the future. Engineers on this team operate with end-to-end ownership, deep technical rigor, and a strong bias toward real-world impact.</p>\n<p>Microsoft Superintelligence Team</p>\n<p>Microsoft Superintelligence team’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.</p>\n<p>This role is part of Microsoft AI’s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models!</p>\n<p>Responsibilities</p>\n<p>Lead the co-design of AI systems across hardware and software boundaries, spanning accelerators, interconnects, memory systems, storage, runtimes, and distributed training/inference frameworks. Drive architectural decisions by analyzing real workloads, identifying bottlenecks across compute, communication, and data movement, and translating findings into actionable system and hardware requirements. Co-design and optimize parallelism strategies, execution models, and distributed algorithms to improve scalability, utilization, reliability, and cost efficiency of large-scale AI systems. Develop and evaluate what-if performance models to project system behavior under future workloads, model architectures, and hardware generations, providing early guidance to hardware and platform roadmaps. Partner with compiler, kernel, and runtime teams to unlock the full performance of current and next-generation accelerators, including custom kernels, scheduling strategies, and memory optimizations. Influence and guide AI hardware design at system and silicon levels, including accelerator microarchitecture, interconnect topology, memory hierarchy, and system integration trade-offs. Lead cross-functional efforts to prototype, validate, and productionize high-impact co-design ideas, working across infrastructure, hardware, and product teams. Mentor senior engineers and researchers, set technical direction, and raise the overall bar for systems rigor, performance engineering, and co-design thinking across the organization.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_a51375e8-30e","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/member-of-technical-staff-software-co-design-ai-hpc-systems-mai-superintelligence-team-3/","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["AI accelerator or GPU architectures","Distributed systems and large-scale AI training/inference","High-performance computing (HPC) and collective communications","ML systems, runtimes, or compilers","Performance modeling, benchmarking, and systems analysis","Hardware–software co-design for AI workloads","Proficiency in systems-level programming (e.g., C/C++, CUDA, Python) and performance-critical software development"],"x-skills-preferred":["Experience designing or operating large-scale AI clusters for training or inference","Deep familiarity with LLMs, multimodal models, or recommendation systems, and their systems-level implications","Experience with accelerator interconnects and communication stacks (e.g., NCCL, MPI, RDMA, high-speed Ethernet or InfiniBand)","Background in performance modeling and capacity planning for future hardware generations","Prior experience contributing to or leading hardware roadmaps, silicon bring-up, or platform architecture reviews","Publications, patents, or open-source contributions in systems, architecture, or ML systems"],"datePosted":"2026-03-08T22:18:41.443Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"AI accelerator or GPU architectures, Distributed systems and large-scale AI training/inference, High-performance computing (HPC) and collective communications, ML systems, runtimes, or compilers, Performance modeling, benchmarking, and systems analysis, Hardware–software co-design for AI workloads, Proficiency in systems-level programming (e.g., C/C++, CUDA, Python) and performance-critical software development, Experience designing or operating large-scale AI clusters for training or inference, Deep familiarity with LLMs, multimodal models, or recommendation systems, and their systems-level implications, Experience with accelerator interconnects and communication stacks (e.g., NCCL, MPI, RDMA, high-speed Ethernet or InfiniBand), Background in performance modeling and capacity planning for future hardware generations, Prior experience contributing to or leading hardware roadmaps, silicon bring-up, or platform architecture reviews, Publications, patents, or open-source contributions in systems, architecture, or ML systems"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d0214534-b6a"},"title":"Senior Applied Scientist","description":"<p>We&#39;re building the next-generation Grounding Service that powers the latest AI applications—chat assistants, copilots, and autonomous agents—with factual, cited, and trustworthy responses. Our platform stitches together retrieval, reasoning, and real-time data so that large language models stay anchored to enterprise knowledge, the public web, and proprietary tools. We&#39;re looking for a Senior Applied Scientist to lead end-to-end science for grounding: inventing retrieval and attribution methods, defining factuality/faithfulness metrics, and shipping production models and APIs that scale to billions of queries. You&#39;ll partner closely with engineering, product, research, and customers to deliver fast, reliable, and explainable answers with source citations across a diverse set of domains and modalities. As a team, we value curiosity, pragmatic rigor, and inclusive collaboration. We believe great systems emerge when scientists and engineers co-design metrics, models, and infrastructure—and when we obsess over customer impact, privacy, and safety. Microsoft&#39;s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction. Responsibilities</p>\n<p>Owns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact. Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images. Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust. Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs. Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web. Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems. Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology. Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property. Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs. Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively. Identifies and solves significant business problems using novel, scalable, and data-driven solutions. Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work. Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review. Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols. Communicates clearly through design documentation, progress updates, and presentations to executives and customers. Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d0214534-b6a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/senior-applied-scientist-37/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Statistics","Econometrics","Computer Science","Electrical or Computer Engineering","Machine Learning","Information Retrieval","Large Language Model Development","Pretraining","Supervised Fine-Tuning","Reinforcement Learning","Optimizing LLM Inference"],"x-skills-preferred":["Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field","6+ years related experience (e.g., statistics, predictive analytics, research)","Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL","Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL)","Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects"],"datePosted":"2026-03-08T22:16:41.766Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Beijing"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, Machine Learning, Information Retrieval, Large Language Model Development, Pretraining, Supervised Fine-Tuning, Reinforcement Learning, Optimizing LLM Inference, Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field, 6+ years related experience (e.g., statistics, predictive analytics, research), Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL, Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL), Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_cd1a0d16-311"},"title":"Member of Technical Staff, Software Co-Design AI HPC Systems","description":"<p>Our team&#39;s mission is to architect, co-design, and productionize next-generation AI systems at datacenter scale. We operate at the intersection of models, systems software, networking, storage, and AI hardware, optimizing end-to-end performance, efficiency, reliability, and cost.</p>\n<p>We pursue this mission through deep hardware–software co-design, combining rigorous systems thinking with hands-on engineering. The team invests heavily in understanding real production workloads large-scale training, inference, and emerging multimodal models and translating those insights into concrete improvements across the stack: from kernels, runtimes, and distributed systems, all the way down to silicon-level trade-offs and datacenter-scale architectures.</p>\n<p>This role sits at the boundary between exploration and production. You will work closely with internal infrastructure, hardware, compiler, and product teams, as well as external partners across the hardware and systems ecosystem. 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Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.</p>\n<p>This role is part of Microsoft AI’s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact.</p>\n<p>Responsibilities\nLead the co-design of AI systems across hardware and software boundaries, spanning accelerators, interconnects, memory systems, storage, runtimes, and distributed training/inference frameworks.</p>\n<p>Drive architectural decisions by analyzing real workloads, identifying bottlenecks across compute, communication, and data movement, and translating findings into actionable system and hardware requirements.</p>\n<p>Co-design and optimize parallelism strategies, execution models, and distributed algorithms to improve scalability, utilization, reliability, and cost efficiency of large-scale AI systems.</p>\n<p>Develop and evaluate what-if performance models to project system behavior under future workloads, model architectures, and hardware generations, providing early guidance to hardware and platform roadmaps.</p>\n<p>Partner with compiler, kernel, and runtime teams to unlock the full performance of current and next-generation accelerators, including custom kernels, scheduling strategies, and memory optimizations.</p>\n<p>Influence and guide AI hardware design at system and silicon levels, including accelerator microarchitecture, interconnect topology, memory hierarchy, and system integration trade-offs.</p>\n<p>Lead cross-functional efforts to prototype, validate, and productionize high-impact co-design ideas, working across infrastructure, hardware, and product teams.</p>\n<p>Mentor senior engineers and researchers, set technical direction, and raise the overall bar for systems rigor, performance engineering, and co-design thinking across the organization.</p>\n<p>Qualifications\nBachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.</p>\n<p>Additional or Preferred Qualifications\nMaster’s Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor’s Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.</p>\n<p>Strong background in one or more of the following areas: AI accelerator or GPU architectures Distributed systems and large-scale AI training/inference High-performance computing (HPC) and collective communications ML systems, runtimes, or compilers Performance modeling, benchmarking, and systems analysis Hardware–software co-design for AI workloads Proficiency in systems-level programming (e.g., C/C++, CUDA, Python) and performance-critical software development.</p>\n<p>Proven ability to work across organizational boundaries and influence technical decisions involving multiple stakeholders. Experience designing or operating large-scale AI clusters for training or inference. Deep familiarity with LLMs, multimodal models, or recommendation systems, and their systems-level implications. Experience with accelerator interconnects and communication stacks (e.g., NCCL, MPI, RDMA, high-speed Ethernet or InfiniBand). Background in performance modeling and capacity planning for future hardware generations. Prior experience contributing to or leading hardware roadmaps, silicon bring-up, or platform architecture reviews. 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Our vision is to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. We&#39;re looking for an experienced HPC Site Reliability Engineer (SRE) to join our High Performance Computing (HPC) infrastructure team. In this role, you&#39;ll blend software engineering and systems engineering to keep our large-scale distributed AI infrastructure reliable and efficient. You&#39;ll ensure that AI systems stay efficient and reliable with very high uptimes.</p>\n<p>Microsoft&#39;s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.</p>\n<p>This role is part of Microsoft AI&#39;s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being.</p>\n<p>Responsibilities\nReliability &amp; Availability : Ensure uptime, resiliency, and fault tolerance of HPC clusters powering MAI model training and inference.\nObservability : Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into all aspects of HPC systems including GPU, clusters, storage and networking.\nAutomation &amp; Tooling : Build automation for deployments, incident response, scaling, and failover in CPU+GPU environments.\nIncident Management : Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements.\nSecurity &amp; Compliance : Ensure data privacy, compliance, and secure operations across model training and serving environments.\nCollaboration : Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows.</p>\n<p>Qualifications\nRequired Qualifications\nMaster’s Degree in Computer Science, Information Technology, or related field AND 2+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR Bachelor’s Degree in Computer Science, Information Technology, or related field AND 4+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR equivalent experience</p>\n<p>Preferred Qualifications\nStrong proficiency in Kubernetes, Docker, and container orchestration.\nKnowledge of CI/CD pipelines for Inference and ML model deployment.\nHands-on experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code.\nExpertise in monitoring &amp; observability tools (Grafana, Datadog, OpenTelemetry, etc.).\nStrong programming/scripting skills in Python, Go, or Bash.\nSolid knowledge of distributed systems, networking, and storage.\nExperience running large-scale GPU clusters for ML/AI workloads (preferred).\nFamiliarity with ML training/inference pipelines.\nExperience with high-performance computing (HPC) and workload schedulers (Kubernetes operators).\nBackground in capacity planning &amp; cost optimization for GPU-heavy environments.</p>\n<p>Work on cutting-edge infrastructure that powers the future of Generative AI. Collaborate with world-class researchers and engineers. Impact millions of users through reliable and responsible AI deployments. Competitive compensation, equity options, and comprehensive benefits.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_93a4ece6-182","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/member-of-technical-staff-site-reliability-engineer-hpc-mai-superintelligence-team/","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$139,900 – $274,800 per year","x-skills-required":["Kubernetes","Docker","container orchestration","CI/CD pipelines","public cloud platforms","infrastructure-as-code","monitoring & observability tools","programming/scripting skills in Python, Go, or Bash","distributed systems","networking","storage","GPU clusters","ML training/inference pipelines","high-performance computing","workload schedulers"],"x-skills-preferred":["strong proficiency in Kubernetes","knowledge of CI/CD pipelines","hands-on experience with public cloud platforms","expertise in monitoring & observability tools","strong programming/scripting skills in Python, Go, or Bash","solid knowledge of distributed systems","experience running large-scale GPU clusters","familiarity with ML training/inference pipelines","experience with high-performance computing"],"datePosted":"2026-03-08T22:09:23.399Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Kubernetes, Docker, container orchestration, CI/CD pipelines, public cloud platforms, infrastructure-as-code, monitoring & observability tools, programming/scripting skills in Python, Go, or Bash, distributed systems, networking, storage, GPU clusters, ML training/inference pipelines, high-performance computing, workload schedulers, strong proficiency in Kubernetes, knowledge of CI/CD pipelines, hands-on experience with public cloud platforms, expertise in monitoring & observability tools, strong programming/scripting skills in Python, Go, or Bash, solid knowledge of distributed systems, experience running large-scale GPU clusters, familiarity with ML training/inference pipelines, experience with high-performance computing","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":139900,"maxValue":274800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7f0a1aea-7d3"},"title":"Head of Agent Ops","description":"<p><strong>Compensation</strong></p>\n<p>$170K – $215K • 0.01% – 0.2%</p>\n<p><strong>Head of Agent Ops</strong></p>\n<p>You&#39;ll own the internal AI infrastructure that makes our team unreasonably fast. That means building, evaluating vendors, and continuously evolving the AI systems our team runs on — with the goal of maximizing every person&#39;s clock speed and scaling our ability to deploy agents across the business.</p>\n<p><strong>Salary Range:</strong></p>\n<p>$170,000–$215,000/year (Range shown is for U.S.-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country’s cost of living. You can explore how we calculate this here: [https://www.firecrawl.dev/careers/compensation](https://www.firecrawl.dev/careers/compensation).)</p>\n<p><strong>Equity Range:</strong></p>\n<p>Up to 0.20%</p>\n<p><strong>Location:</strong></p>\n<p>San Francisco, CA (Hybrid); Truly exceptional remote considered.</p>\n<p><strong>Job Type:</strong></p>\n<p>Full-Time (SF)</p>\n<p><strong>Experience:</strong></p>\n<p>5+ years or equivalent shipped systems</p>\n<p><strong>Visa:</strong></p>\n<p>US Citizenship/Visa required for SF</p>\n<p><strong>About Firecrawl</strong></p>\n<p>Firecrawl is the easiest way to extract data from the web. Developers use us to reliably convert URLs into LLM-ready markdown or structured data with a single API call.</p>\n<p><strong>What We&#39;re Looking For</strong></p>\n<p><strong>A technical practitioner, not a theorist.</strong></p>\n<p>You understand how models actually work — not just API calls, but attention, context windows, inference tradeoffs, tool use patterns. You read papers. But your first instinct is always to build, not to theorize. You&#39;ve shipped real systems that automate real work.</p>\n<p><strong>Someone with battle-tested opinions on AI-assisted development.</strong></p>\n<p>You&#39;ve pushed vibe coding techniques far enough to know where they break. You have strong, experience-driven opinions about what works and what doesn&#39;t — grounded in first principles, not hype. You know when to let the model drive and when to take the wheel.</p>\n<p><strong>An automation obsessive.</strong></p>\n<p>You&#39;ve already automated your own life to a degree that others find somewhat unhinged. You see manual processes the way most people see bugs — something that shouldn&#39;t exist and won&#39;t for long.</p>\n<p><strong>Someone who understands the bitter lesson.</strong></p>\n<p>You know where the highest-leverage opportunities are because you understand which problems get solved by scale and which don&#39;t. You allocate your energy accordingly.</p>\n<p><strong>$10k+/month token budget</strong></p>\n<p>(will increase with demonstrated impact).</p>\n<p><strong>What We&#39;re NOT Looking For</strong></p>\n<p><strong>AI skeptics.</strong></p>\n<p>If you think superintelligence is hundreds of years away, this isn&#39;t your role.</p>\n<p><strong>Comfort optimizers.</strong></p>\n<p>We&#39;re looking for people who seek discomfort and aim to make a difference — not optimize for comfy vibes.</p>\n<p><strong>Status chasers.</strong></p>\n<p>If you&#39;re here because AI is in the news, you&#39;re not a fit. If you believe we&#39;re at a once-in-a-species inflection point and you want to shape the wave, not just ride it — this is the place for you.</p>\n<p><strong>A Note On Pace</strong></p>\n<p>We operate at an absurd level of urgency because the window for what we&#39;re building won&#39;t stay open forever. If that excites you, keep reading. If it doesn&#39;t, no hard feelings — but this role probably isn&#39;t for you.</p>\n<p><strong>Benefits &amp; Perks</strong></p>\n<p><strong>Available to all employees</strong></p>\n<ul>\n<li><strong>Salary that makes sense</strong> — $170,000–215,000/year (SF, U.S.-based), based on impact, not tenure</li>\n</ul>\n<ul>\n<li><strong>Own a piece</strong> — Up to 0.20% equity in what you&#39;re helping build</li>\n</ul>\n<ul>\n<li><strong>Generous PTO</strong> — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge</li>\n</ul>\n<ul>\n<li><strong>Parental leave</strong> — 12 weeks fully paid, for moms and dads</li>\n</ul>\n<ul>\n<li><strong>Wellness stipend</strong> — $100/month for the gym, therapy, massages, or whatever keeps you human</li>\n</ul>\n<ul>\n<li><strong>Learning &amp; Development</strong> \\- Expense up to $1000/year toward anything that helps you grow professionally</li>\n</ul>\n<ul>\n<li><strong>Team offsites</strong> — A change of scenery, minus the trust falls</li>\n</ul>\n<ul>\n<li><strong>Sabbatical</strong>— 3 paid months off after 4 years, do something fun and new</li>\n</ul>\n<p><strong>Available to US-based full-time employees</strong></p>\n<ul>\n<li><strong>Full coverage, no red tape</strong> — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works</li>\n</ul>\n<ul>\n<li><strong>Life &amp; Disability insurance</strong> — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life&#39;s curveballs</li>\n</ul>\n<ul>\n<li><strong>Supplemental options</strong> — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind</li>\n</ul>\n<ul>\n<li><strong>Doctegrity telehealth</strong> — Talk to a doctor from your couch</li>\n</ul>\n<ul>\n<li><strong>401(k) plan</strong> — Retirement might be a ways off, but future-you will thank you</li>\n</ul>\n<ul>\n<li><strong>Pre-tax benefits</strong> — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit</li>\n</ul>\n<ul>\n<li><strong>Pet insurance</strong> — Because fur babies are family too</li>\n</ul>\n<p><strong>Available to SF-based employees</strong></p>\n<ul>\n<li><strong>SF HQ perks</strong> — Snacks, drinks, team lunches, intense ping pong, and peak startup energy</li>\n</ul>\n<ul>\n<li><strong>E-Bike transportation</strong> — A loaner electric bike to get you around the city, on us</li>\n</ul>\n<p><strong>Interview Process</strong></p>\n<ul>\n<li><strong>Application Review</strong> – Send us your stuff + a quick note on why this excites you (plus links to things you’ve built).</li>\n</ul>\n<ul>\n<li><strong>Intro Chat</strong> <strong>(~20–25 min)</strong> – Quick alignment call focused on what you’ve shipped, how you think about agents vs workflows, and what you’d automate first at Firecrawl.</li>\n</ul>\n<ul>\n<li><strong>Async Systems Design</strong> <strong>(60–90 min)</strong> – A short, structured design exercise: propose a v1 agent/workflow to eliminate a real internal bottleneck. We’re looking for taste, guardrails, eval/monitoring, and a rollout plan — not a big build.</li>\n</ul>\n<ul>\n<li><strong>Founder Chat</strong> <strong>(~30 min)</strong> – Culture, pace, ownership, and how you like to work. Time for your questions too.</li>\n</ul>\n<ul>\n<li><strong>Paid Work Trial</strong> <strong>(1–2 weeks)</strong> – Test drive the real thing: ship something real, get feedback, and iterate.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_7f0a1aea-7d3","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Firecrawl","sameAs":"https://jobs.ashbyhq.com","logo":"https://logos.yubhub.co/firecrawl.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/firecrawl/d8122162-754a-47fb-9ce7-51fdf6320a3f","x-work-arrangement":"Hybrid","x-experience-level":"senior","x-job-type":"Full time","x-salary-range":"$170K – $215K • 0.01% – 0.2%","x-skills-required":["AI infrastructure","vendor evaluation","continuous evolution","agent systems","workflow automation","design","evaluation","monitoring","rollout plan"],"x-skills-preferred":["attention","context windows","inference tradeoffs","tool use patterns","vibe coding techniques","automation","scale","energy allocation"],"datePosted":"2026-03-08T18:40:09.848Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA (Hybrid)"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"AI infrastructure, vendor evaluation, continuous evolution, agent systems, workflow automation, design, evaluation, monitoring, rollout plan, attention, context windows, inference tradeoffs, tool use patterns, vibe coding techniques, automation, scale, energy allocation","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":170000,"maxValue":215000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7b2b97d5-0a1"},"title":"Software Engineer, Inference Deployment","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</p>\n<p><strong>About the Role</strong></p>\n<p>Our mandate is to make inference deployment boring and unattended.</p>\n<p>Anthropic serves Claude to millions of users across GPUs, TPUs, and Trainium — and every model update must reach production safely, quickly, and without disrupting service. We&#39;re building the systems that make inference deployment continuous and unattended.</p>\n<p>As a Software Engineer on the Launch Engineering team, you&#39;ll design and build the deployment infrastructure that moves inference code from merge to production. This is a resource-constrained optimization problem at its core: validation and deployment consume the same accelerator chips that serve customer traffic — your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, so the system must adapt continuously. You&#39;ll build systems that navigate these constraints — orchestrating validation, scheduling deployments intelligently, and driving down cycle time from merge to production.</p>\n<p>If you&#39;ve built deployment systems at scale and gravitate toward the hardest problems at the intersection of automation and resource management, this team will give you an outsized scope to work on them.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li><strong>Own deployment orchestration</strong> that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions</li>\n<li><strong>Improve capacity-aware deployment scheduling</strong> to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes</li>\n<li><strong>Extend deployment observability</strong> — dashboards and tooling that answer &quot;what code is running in production,&quot; &quot;where is my commit,&quot; and &quot;what validation passed for this deploy&quot;</li>\n<li><strong>Drive down cycle time</strong> from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism</li>\n<li><strong>Optimize fleet rollout strategies</strong> for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity</li>\n<li><strong>Evolve self-service model onboarding</strong> so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement</li>\n<li><strong>Partner across the Inference organization</strong> with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems</li>\n</ul>\n<p><strong>You May Be a Good Fit If You Have</strong></p>\n<ul>\n<li>5+ years of experience building deployment, release, or delivery infrastructure at scale</li>\n<li>Strong software engineering skills with experience designing systems that manage complex state machines and multi-stage pipelines</li>\n<li>Experience with deployment systems where resource constraints shape the design — whether that&#39;s fleet capacity, network bandwidth, hardware availability, or coordinated rollout windows</li>\n<li>A track record of building automation that measurably improves deployment velocity and reliability</li>\n<li>Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration</li>\n<li>Comfort working across the stack — from backend services and databases to CLI tools and web UIs</li>\n<li>Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners</li>\n</ul>\n<p><strong>Strong Candidates May Also Have</strong></p>\n<ul>\n<li>Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium)</li>\n<li>Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity)</li>\n<li>Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback</li>\n<li>Experience at companies with large-scale release engineering challenges (mobile release trains, monorepo deployments, multi-datacenter rollouts)</li>\n<li>Experience with Python and/or Rust in production systems</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_7b2b97d5-0a1","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5111745008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000 - $485,000USD","x-skills-required":["deployment","release","delivery","infrastructure","Kubernetes","container","orchestration","pipelines","state machines","multi-stage","pipelines","parallelism","optimization","resource management","automation","velocity","reliability","communication","collaboration","oncall","model teams","infrastructure partners"],"x-skills-preferred":["ML inference","training infrastructure","capacity planning","resource-constrained scheduling","bin-packing","fleet management","job scheduling","hardware affinity","progressive delivery","canary/soak testing","blue-green deployments","traffic shifting","automated rollback","mobile release trains","monorepo deployments","multi-datacenter rollouts","Python","Rust"],"datePosted":"2026-03-08T13:54:19.012Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"deployment, release, delivery, infrastructure, Kubernetes, container, orchestration, pipelines, state machines, multi-stage, pipelines, parallelism, optimization, resource management, automation, velocity, reliability, communication, collaboration, oncall, model teams, infrastructure partners, ML inference, training infrastructure, capacity planning, resource-constrained scheduling, bin-packing, fleet management, job scheduling, hardware affinity, progressive delivery, canary/soak testing, blue-green deployments, traffic shifting, automated rollback, mobile release trains, monorepo deployments, multi-datacenter rollouts, Python, Rust","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_25934fbc-c50"},"title":"Staff / Senior Software Engineer, Cloud Inference","description":"<p><strong>About the Role</strong></p>\n<p>The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform—from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.</p>\n<p>Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic&#39;s most precious resources—compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need engineers who can navigate these platform differences, build robust abstractions that work across providers, and make smart infrastructure decisions that keep us cost-effective at massive scale.</p>\n<p>Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.</p>\n<p><strong>What You&#39;ll Do</strong></p>\n<ul>\n<li>Design and build infrastructure that serves Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models</li>\n<li>Collaborate with CSP partner engineering teams to resolve operational issues, influence provider roadmaps, and stand up end-to-end serving on new cloud platforms</li>\n<li>Design and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions</li>\n<li>Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity</li>\n<li>Contribute to capacity planning and autoscaling strategies that dynamically match supply with demand across CSP validation and production workloads</li>\n<li>Optimize inference cost and performance across providers—designing workload placement and routing systems that direct requests to the most cost-effective accelerator and region</li>\n<li>Contribute to inference features that must work consistently across all platforms</li>\n<li>Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads</li>\n</ul>\n<p><strong>You May Be a Good Fit If You:</strong></p>\n<ul>\n<li>Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users</li>\n<li>Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code or container orchestration</li>\n<li>Have strong interest in inference</li>\n<li>Thrive in cross-functional collaboration with both internal teams and external partners</li>\n<li>Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems</li>\n<li>Are highly autonomous and self-driven, taking ownership of problems end-to-end with a bias toward flexibility and high-impact work</li>\n<li>Pick up slack, even when it goes outside your job description</li>\n</ul>\n<p><strong>Strong Candidates May Also Have Experience With</strong></p>\n<ul>\n<li>Direct experience working with CSP partner teams to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings</li>\n<li>A background in building platform-agnostic tooling or abstraction layers that work across cloud providers</li>\n<li>Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments</li>\n<li>Strong familiarity with LLM inference optimization, batching, caching, and serving strategies</li>\n<li>Experience with Machine learning infrastructure including GPUs, TPUs, Trainium, or other AI accelerators</li>\n<li>Background designing and building CI/CD systems that automate deployment and validation across cloud environments</li>\n<li>Solid understanding of multi-region deployments, geographic routing, and global traffic management</li>\n<li>Proficiency in Python or Rust</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_25934fbc-c50","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5107466008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$300,000 - $485,000 USD","x-skills-required":["Software engineering","Cloud infrastructure","Kubernetes","Infrastructure as Code","Container orchestration","LLM inference optimization","Batching","Caching","Serving strategies","Machine learning infrastructure","GPUs","TPUs","Trainium","AI accelerators","CI/CD systems","Deployment and validation","Cloud environments","Multi-region deployments","Geographic routing","Global traffic management"],"x-skills-preferred":["Python","Rust","Cloud platforms","Networking","Security","Privacy","Billing","Managed service offerings","Platform-agnostic tooling","Abstraction layers","Capacity management","Cost optimization","Resource planning"],"datePosted":"2026-03-08T13:49:59.956Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Software engineering, Cloud infrastructure, Kubernetes, Infrastructure as Code, Container orchestration, LLM inference optimization, Batching, Caching, Serving strategies, Machine learning infrastructure, GPUs, TPUs, Trainium, AI accelerators, CI/CD systems, Deployment and validation, Cloud environments, Multi-region deployments, Geographic routing, Global traffic management, Python, Rust, Cloud platforms, Networking, Security, Privacy, Billing, Managed service offerings, Platform-agnostic tooling, Abstraction layers, Capacity management, Cost optimization, Resource planning","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":300000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_f95fe525-8fd"},"title":"Staff Software Engineer, Inference","description":"<p><strong>About the role</strong></p>\n<p>Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.</p>\n<p><strong>As a Staff Software Engineer on our Inference team, you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.</strong></p>\n<p><strong>Strong candidates may also have experience with</strong></p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP)</li>\n<li>Python or Rust</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Representative projects across the org</strong></p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p><strong>Deadline to apply: None. Applications will be reviewed on a rolling basis.</strong></p>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</li>\n</ul>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</strong></p>\n<p><strong>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f95fe525-8fd","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5097742008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"£325,000 - £390,000GBP","x-skills-required":["performance optimization","distributed systems","large-scale service orchestration","intelligent request routing","LLM inference optimization","batching strategies","multi-accelerator deployments","Kubernetes","cloud infrastructure","Python","Rust"],"x-skills-preferred":["high-performance, large-scale distributed systems","implementing and deploying machine learning systems at scale","load balancing, request routing, or traffic management systems","caching strategies"],"datePosted":"2026-03-08T13:49:42.673Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"performance optimization, distributed systems, large-scale service orchestration, intelligent request routing, LLM inference optimization, batching strategies, multi-accelerator deployments, Kubernetes, cloud infrastructure, Python, Rust, high-performance, large-scale distributed systems, implementing and deploying machine learning systems at scale, load balancing, request routing, or traffic management systems, caching strategies","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":325000,"maxValue":390000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ca53b3f7-f72"},"title":"Staff / Senior Software Engineer, Inference","description":"<p><strong>About the role</strong></p>\n<p>Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>The team has a dual mandate: <strong>maximizing compute efficiency</strong> to serve our explosive customer growth, while <strong>enabling breakthrough research</strong> by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.</p>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Enjoy pair programming (we love to pair!)</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP, Azure)</li>\n<li>Python or Rust</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p><strong>Deadline to apply:</strong></p>\n<p>None. Applications will be reviewed on a rolling basis.</p>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</strong></p>\n<p><strong>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p>The easiest way to understand our research directions is to read our recent research. This research co</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ca53b3f7-f72","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4951696008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$300,000 - $485,000 USD","x-skills-required":["distributed systems","machine learning systems","load balancing","request routing","traffic management","LLM inference optimization","Kubernetes","cloud infrastructure","Python","Rust"],"x-skills-preferred":["high-performance distributed systems","implementing and deploying machine learning systems at scale","structured sampling","prompt caching"],"datePosted":"2026-03-08T13:49:03.736Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, machine learning systems, load balancing, request routing, traffic management, LLM inference optimization, Kubernetes, cloud infrastructure, Python, Rust, high-performance distributed systems, implementing and deploying machine learning systems at scale, structured sampling, prompt caching","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":300000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_459d7a0d-23e"},"title":"Technical Program Manager, Inference Performance","description":"<p>As a Technical Program Manager for Inference, you&#39;ll be the critical bridge between our inference systems and the broader organisation. You&#39;ll drive strategic initiatives across inference runtime and accelerator performance—coordinating model launches, managing cross-platform dependencies, and ensuring reliability across multiple hardware targets.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li><strong>Systems Integration &amp; Coordination</strong>: Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels between teams. Drive end-to-end planning for major infrastructure transitions including platform modernization and new tech adoption.</li>\n<li><strong>Performance &amp; Efficiency:</strong> Partner with engineering teams to identify optimisation opportunities, track performance metrics, and prioritise work that unlocks capacity gains. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability.</li>\n<li><strong>Launch Coordination:</strong> Drive end-to-end readiness for model and feature launches across multiple hardware platforms. Establish processes for cross-platform validation, manage launch timelines, and ensure smooth handoffs between runtime, accelerator, and downstream teams.</li>\n<li><strong>Strategic Planning:</strong> Own and prioritise the inference deployment roadmap, working closely with engineering leadership to prioritise initiatives and manage dependencies. Provide visibility into upcoming changes and their organisational impact.</li>\n<li><strong>Stakeholder Communication:</strong> Build strong relationships across research, engineering, and product teams to understand requirements and constraints. Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines.</li>\n<li><strong>Process Improvement:</strong> Identify inefficiencies in current workflows and drive systematic improvements. Establish metrics and dashboards to track infrastructure health, capacity utilisation, and deployment success rates.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems</li>\n<li>Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks.</li>\n<li>Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives</li>\n<li>Have strong stakeholder management skills and can build trust with both technical and non-technical partners</li>\n<li>Are comfortable navigating competing priorities and using data to drive technical decisions</li>\n<li>Have experience with infrastructure scaling initiatives, hardware integrations, or deployment governance</li>\n<li>Thrive in fast-paced environments and can balance strategic planning with tactical execution</li>\n<li>Are passionate about AI infrastructure and understand the unique challenges of deploying and scaling large language models</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li><strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</li>\n<li><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</li>\n</ul>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_459d7a0d-23e","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5107763008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$290,000 - $365,000USD","x-skills-required":["technical program management","inference systems","compilers","hardware accelerators","cross-functional initiatives","model launches","cross-platform dependencies","reliability","performance metrics","capacity gains","efficiency wins","runtime","accelerator layers","launch timelines","smooth handoffs","strategic planning","inference deployment roadmap","engineering leadership","prioritisation","dependencies","visibility","upcoming changes","organisational impact","stakeholder communication","requirements","constraints","technical complexities","clear updates","leadership","alignment","priorities","timelines","process improvement","inefficiencies","workflows","systematic improvements","metrics","dashboards","infrastructure health","capacity utilisation","deployment success rates"],"x-skills-preferred":["infrastructure scaling initiatives","hardware integrations","deployment governance","fast-paced environments","strategic planning","tactical execution","AI infrastructure","large language models"],"datePosted":"2026-03-08T13:48:59.030Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"technical program management, inference systems, compilers, hardware accelerators, cross-functional initiatives, model launches, cross-platform dependencies, reliability, performance metrics, capacity gains, efficiency wins, runtime, accelerator layers, launch timelines, smooth handoffs, strategic planning, inference deployment roadmap, engineering leadership, prioritisation, dependencies, visibility, upcoming changes, organisational impact, stakeholder communication, requirements, constraints, technical complexities, clear updates, leadership, alignment, priorities, timelines, process improvement, inefficiencies, workflows, systematic improvements, metrics, dashboards, infrastructure health, capacity utilisation, deployment success rates, infrastructure scaling initiatives, hardware integrations, deployment governance, fast-paced environments, strategic planning, tactical execution, AI infrastructure, large language models","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":290000,"maxValue":365000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_82a4d6f7-01c"},"title":"Staff Research Engineer, Discovery Team","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</p>\n<p><strong>About the Team</strong></p>\n<p>Our team is organised around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. Our team likes to think across the whole model stack. Currently the team is focused on improving models&#39; abilities to use computers – as a laboratory for long horizon tasks and a key blocker to many scientific workflows.</p>\n<p><strong>About the role</strong></p>\n<p>As a Research Engineer on our team you will work end to end, identifying and addressing key blockers on the path to scientific AGI. Strong candidates should have familiarity with language model training, evaluation, and inference, be comfortable triaging research ideas and diagnosing problems and enjoy working collaboratively. Familiarity with performance optimisation, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines is highly encouraged.</p>\n<p>Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Working across the full stack to identify and remove bottlenecks preventing progress toward scientific AGI</li>\n<li>Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery</li>\n<li>Scaling research ideas from prototype to production</li>\n<li>Create benchmarks and evaluation frameworks to measure model capabilities in scientific workflows and computer use</li>\n<li>Implement distributed training systems and performance optimisations to support large-scale model development</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have 8+ years of ML research experience</li>\n<li>Are familiar with large scale language model training, evaluation, and inference pipelines</li>\n<li>Enjoy obsessively iterating on immediate blockers towards longterm goals</li>\n<li>Thrive working collaboratively to solve problems</li>\n<li>Have expertise in performance optimisation and distributed computing systems</li>\n<li>Show strong problem-solving skills and ability to identify technical bottlenecks in complex systems</li>\n<li>Can translate research concepts into scalable engineering solutions</li>\n<li>Have a track record of shipping ML systems that tackle challenging multi-step reasoning problems</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Expertise with performance optimisation for language model inference and training</li>\n<li>Experience with computer use automation and agentic AI systems</li>\n<li>A history working on reinforcement learning approaches for complex task completion</li>\n<li>Knowledge of containerisation technologies (Docker, Kubernetes) and cloud deployment at scale</li>\n<li>Demonstrated ability to work across multiple domains (language modelling, systems engineering, scientific computing)</li>\n<li>Have experience with VM/sandboxing/container deployment and large-scale data processing</li>\n<li>Experience working with large scale data problem solving and infrastructure</li>\n<li>Published research or practical experience in scientific AI applications or long-horizon reasoning</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.** Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.</strong></p>\n<p><strong>Your safety matters to us.** To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. 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We view AI research as an empirical science.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_82a4d6f7-01c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4593216008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000USD","x-skills-required":["language model training","evaluation","inference","performance optimisation","distributed systems","vm/sandboxing/container deployment","large scale data pipelines"],"x-skills-preferred":["performance optimisation for language model inference and training","computer use automation and agentic AI systems","reinforcement learning approaches for complex task completion","containerisation technologies (Docker, Kubernetes) and cloud deployment at scale","VM/sandboxing/container deployment and large-scale data processing"],"datePosted":"2026-03-08T13:47:19.194Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"language model training, evaluation, inference, performance optimisation, distributed systems, vm/sandboxing/container deployment, large scale data pipelines, performance optimisation for language model inference and training, computer use automation and agentic AI systems, reinforcement learning approaches for complex task completion, containerisation technologies (Docker, Kubernetes) and cloud deployment at scale, VM/sandboxing/container deployment and large-scale data processing","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_da726093-b19"},"title":"Research Engineer, Discovery","description":"<p><strong>About the Role</strong></p>\n<p>As a Research Engineer on our team, you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments</li>\n<li>Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities</li>\n<li>Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.</li>\n<li>Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows</li>\n<li>Collaborate to translate experimental requirements into production-ready infrastructure</li>\n<li>Develop large scale data pipelines to handle advanced language model training requirements</li>\n<li>Optimize large scale training and inference pipelines for stable and efficient reinforcement learning</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems</li>\n<li>Are a strong communicator and enjoy working collaboratively</li>\n<li>Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads</li>\n<li>Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale</li>\n<li>Have proven track record of building large-scale data pipelines and distributed storage systems</li>\n<li>Excel at diagnosing and resolving complex infrastructure challenges in production environments</li>\n<li>Can work effectively across the full ML stack from data pipelines to performance optimization</li>\n<li>Have experience collaborating with other researchers to scale experimental ideas</li>\n<li>Thrive in fast-paced environments and can rapidly iterate from experimentation to production</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)</li>\n<li>Background in building infrastructure for AI research labs or large-scale ML organizations</li>\n<li>Knowledge of GPU/TPU architectures and language model inference optimization</li>\n<li>Experience with cloud platforms (AWS, GCP) at enterprise scale</li>\n<li>Familiarity with VM and container orchestration.</li>\n<li>Experience with workflow orchestration tools and experiment management systems</li>\n<li>History working with large scale reinforcement learning</li>\n<li>Comfort with large scale data pipelines (Beam, Spark, Dask, …)</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</li>\n</ul>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</strong></p>\n<p><strong>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale projects, and we&#39;re committed to making a positive impact on the world.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_da726093-b19","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4669581008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000 USD","x-skills-required":["infrastructure engineering","large-scale distributed systems","performance optimization","containerization technologies","orchestration at scale","data pipelines","distributed storage systems","complex infrastructure challenges","ML stack","workflow orchestration tools","experiment management systems","reinforcement learning","large scale data pipelines"],"x-skills-preferred":["language model training infrastructure","distributed ML frameworks","GPU/TPU architectures","language model inference optimization","cloud platforms","VM and container orchestration","workflow orchestration tools","experiment management systems","large scale reinforcement learning","large scale data pipelines"],"datePosted":"2026-03-08T13:46:32.661Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"infrastructure engineering, large-scale distributed systems, performance optimization, containerization technologies, orchestration at scale, data pipelines, distributed storage systems, complex infrastructure challenges, ML stack, workflow orchestration tools, experiment management systems, reinforcement learning, large scale data pipelines, language model training infrastructure, distributed ML frameworks, GPU/TPU architectures, language model inference optimization, cloud platforms, VM and container orchestration, workflow orchestration tools, experiment management systems, large scale reinforcement learning, large scale data pipelines","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c33b2d78-cc9"},"title":"Research Lead, Training Insights","description":"<p><strong>About the role</strong></p>\n<p>As a Research Lead on the Training Insights team, you&#39;ll develop the strategy for, and lead execution on, how we measure and characterise model capabilities across training and deployment. This is a hands-on leadership role: you&#39;ll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.</p>\n<p>Your work will span the full lifecycle of model development. You&#39;ll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You&#39;ll also take a cross-organisational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.</p>\n<p>This role carries significant visibility and impact. You&#39;ll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Build new novel and long-horizon evaluations</li>\n<li>Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training</li>\n<li>Lead strategic evaluation coverage across the company</li>\n<li>Shape the evaluation narrative for model releases</li>\n<li>Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research</li>\n<li>Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules</li>\n<li>Build and maintain relationships across Anthropic&#39;s research organisation to ensure evaluation insights inform training and deployment decisions</li>\n<li>Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant experience designing and running evaluations for large language models or similar complex ML systems</li>\n<li>Have led technical projects or teams, either formally or through sustained ownership of critical research directions</li>\n<li>Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly</li>\n<li>Think strategically about what to measure and why, not just how to measure it</li>\n<li>Can synthesise information across multiple teams and workstreams to form a coherent picture of model capabilities</li>\n<li>Communicate complex technical findings clearly to both technical and non-technical audiences</li>\n<li>Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings</li>\n<li>Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Experience building evaluations for long-horizon or agentic tasks</li>\n<li>Deep familiarity with Reinforcement Learning training dynamics and how model behaviour changes during training</li>\n<li>Published research in machine learning evaluation, benchmarking, or related areas</li>\n<li>Experience with safety evaluation frameworks and red teaming methodologies</li>\n<li>Background in psychometrics, experimental psychology, or other measurement-focused disciplines</li>\n<li>A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment</li>\n<li>Experience managing or mentoring researchers and engineers</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions</li>\n<li>Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge</li>\n<li>Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritising new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product</li>\n<li>Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterise model capabilities and limitations</li>\n<li>Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks</li>\n<li>Leading a team effort to build reusable evaluation infrastructure that serves multiple teams across the research organisation</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices repsectively.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas!</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c33b2d78-cc9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5139654008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$850,000 - $850,000USD","x-skills-required":["machine learning","evaluation methodologies","Reinforcement Learning","Pretraining","Inference","Product","Alignment","Safeguards"],"x-skills-preferred":["psychometrics","experimental psychology","safety evaluation frameworks","red teaming methodologies"],"datePosted":"2026-03-08T13:45:37.187Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, evaluation methodologies, Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, psychometrics, experimental psychology, safety evaluation frameworks, red teaming methodologies","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":850000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_325c968b-d59"},"title":"Inference Technical Lead, Sora","description":"<p><strong>Inference Technical Lead, Sora</strong></p>\n<p><strong>Location</strong></p>\n<p>San Francisco</p>\n<p><strong>Employment Type</strong></p>\n<p>Full time</p>\n<p><strong>Location Type</strong></p>\n<p>Hybrid</p>\n<p><strong>Department</strong></p>\n<p>Research</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$380K • Offers Equity</li>\n</ul>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.</p>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p><strong>About the Team</strong></p>\n<p>The Sora team is pioneering multimodal capabilities for OpenAI’s foundation models. We’re a hybrid research and product team focused on integrating multimodal functionalities into our AI products, ensuring they are reliable, user-friendly, and aligned with our mission of broad societal benefit.</p>\n<p><strong>About the Role</strong></p>\n<p>We’re looking for a GPU Inference Engineer to contribute to improvements in model serving efficiency for Sora. This is a high-impact role where you’ll drive initiatives to optimize inference performance and scalability. You’ll also be engaged in model design, to help assist our researchers in developing inference-friendly models.</p>\n<p>_<strong>This role is critical to scaling the team’s broader goals - it will directly enable leadership to focus on higher-leverage initiatives by building a stronger technical foundation.</strong>_</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Perform engineering efforts focused on improving model serving, inference performance, and system efficiency</li>\n</ul>\n<ul>\n<li>Drive optimizations from a kernel and data movement perspective to improve system throughput and reliability</li>\n</ul>\n<ul>\n<li>Partner closely with research and product teams to ensure our models perform effectively at scale</li>\n</ul>\n<ul>\n<li>Design, build, and improve critical serving infrastructure to support Sora’s growth and reliability needs</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Have deep expertise in model performance optimization, particularly at the inference layer</li>\n</ul>\n<ul>\n<li>Have a strong background in kernel-level systems, data movement, and low-level performance tuning</li>\n</ul>\n<ul>\n<li>Are excited about scaling high-performing AI systems that serve real-world, multimodal workloads</li>\n</ul>\n<ul>\n<li>Can navigate ambiguity, set technical direction, and drive complex initiatives to completion</li>\n</ul>\n<p>_<strong>This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</strong>_</p>\n<p><strong>About OpenAI</strong></p>\n<p>OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_325c968b-d59","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenAI","sameAs":"https://jobs.ashbyhq.com","logo":"https://logos.yubhub.co/openai.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openai/3c2d1178-777f-4613-a084-75a3d37cd1af","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$380K • Offers Equity","x-skills-required":["GPU Inference Engineer","Model Performance Optimization","Kernel-Level Systems","Data Movement","Low-Level Performance Tuning"],"x-skills-preferred":["AI Systems","Multimodal Workloads","Complex Initiatives","Technical Direction"],"datePosted":"2026-03-06T18:42:26.117Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"GPU Inference Engineer, Model Performance Optimization, Kernel-Level Systems, Data Movement, Low-Level Performance Tuning, AI Systems, Multimodal Workloads, Complex Initiatives, Technical Direction","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":380000,"maxValue":380000,"unitText":"YEAR"}}}]}