{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/jax"},"x-facet":{"type":"skill","slug":"jax","display":"Jax","count":95},"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. 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The estimated base salary range for this position is $175,000 to $250,000, which is specific to New York and may change in the future.</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_867e3558-9a7","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Equity IT","sameAs":"https://mlp.eightfold.ai","logo":"https://logos.yubhub.co/mlp.eightfold.ai.png"},"x-apply-url":"https://mlp.eightfold.ai/careers/job/755955412056","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$175,000 to $250,000","x-skills-required":["Core Java","Java Swing","HTML","JavaScript","CSS","JQuery","Multithreading","Distributed systems","Unit testing frameworks","Continuous test-driven development practices","MVC","MV","MVP","RESTful web services"],"x-skills-preferred":["EXT-JS","AngularJS","AJAX","JSON","Equities","Futures","Options","OMS architecture and design","Messaging middleware","Solace","Relational databases","NoSQL databases","MongoDB","Financial data","Cloud","AWS","GCP","Azure"],"datePosted":"2026-04-18T22:13:00.318Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Miami, Florida, United States of America · New York, New York, United States of America"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Finance","skills":"Core Java, Java Swing, HTML, JavaScript, CSS, JQuery, Multithreading, Distributed systems, Unit testing frameworks, Continuous test-driven development practices, MVC, MV, MVP, RESTful web services, EXT-JS, AngularJS, AJAX, JSON, Equities, Futures, Options, OMS architecture and design, Messaging middleware, Solace, Relational databases, NoSQL databases, MongoDB, Financial data, Cloud, AWS, GCP, Azure","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":175000,"maxValue":250000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1c4de3ab-a58"},"title":"Machine Learning Engineer, Global Public Sector","description":"<p>We&#39;re hiring a Machine Learning Engineer to bridge the gap between frontier research and real-world impact. As a key member of our GPS Engineering team, you will lead the charge in research into Agent design, Deep Research and AI Safety/reliability, developing novel methodologies that not only power public sector applications but set new standards across the entire Scale organisation.</p>\n<p>Your mission is threefold:</p>\n<ul>\n<li>Frontier Research &amp; Publication: Leading research into LLM/agent capabilities, reasoning, and safety, with the goal of publishing at top-tier venues (NeurIPS, ICML, ICLR).</li>\n<li>Cross-Org Impact: Developing generalised techniques in Agent design, AI Safety and Deep Research agents that scale across our commercial and government platforms.</li>\n<li>Mission-Critical Applications: Engineering high-stakes AI systems that impact millions of citizens globally.</li>\n</ul>\n<p>You will:</p>\n<ul>\n<li>Pioneer Novel Architectures: Design and train state-of-the-art models and agents, moving beyond “off-the-shelf” solutions to create custom architectures for complex public sector reasoning tasks.</li>\n<li>Lead AI Safety Initiatives: Research and implement robust safety frameworks, including red teaming, alignment (RLHF/DPO), and bias mitigation strategies essential for sovereign AI.</li>\n<li>Drive Deep Research Capabilities: Develop agents capable of long-horizon reasoning and autonomous information synthesis to solve complex problems for national security and public policy.</li>\n<li>Publish and Contribute: Represent Scale in the broader research community by publishing high-impact papers and contributing to open-source breakthroughs.</li>\n<li>Consult as a Subject Matter Expert: Act as a technical authority for public sector leaders, advising on the theoretical limits and safety requirements of emerging AI.</li>\n<li>Build Evaluation Frontiers: Create new benchmarks and evaluation protocols that define what success looks like for high-stakes, non-commercial AI applications.</li>\n</ul>\n<p>Ideally, you’d have:</p>\n<ul>\n<li>Advanced Degree: PhD or Master’s in Computer Science, Mathematics, or a related field with a focus on Deep Learning.</li>\n<li>Research Track Record: A portfolio of first-author publications at major conferences (NeurIPS, ICML, CVPR, EMNLP, etc.).</li>\n<li>Engineering Rigour: Strong proficiency in Python, deep learning frameworks (PyTorch/JAX), with the ability to write production-ready code that scales.</li>\n<li>Safety Expertise: Experience in alignment, robustness, or interpretability research.</li>\n</ul>\n<p>Nice to haves:</p>\n<ul>\n<li>Experience with large-scale distributed training on massive clusters.</li>\n<li>Experience in building agentic systems that are reliable.</li>\n<li>Experience in Sovereign AI or working with highly regulated data environments.</li>\n<li>A zero-to-one mindset: Comfortable navigating ambiguity and defining research directions from scratch.</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_1c4de3ab-a58","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/4413274005","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","Deep Learning","PyTorch","JAX","AI Safety","Alignment","Robustness","Interpretability"],"x-skills-preferred":["Large-scale Distributed Training","Agentic Systems","Sovereign AI","Regulated Data Environments"],"datePosted":"2026-04-18T15:59:21.005Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Doha, Qatar; London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep Learning, PyTorch, JAX, AI Safety, Alignment, Robustness, Interpretability, Large-scale Distributed Training, Agentic Systems, Sovereign AI, Regulated Data Environments"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5aa5b947-f4d"},"title":"Staff Machine Learning Research Scientist/ Engineer, Agents","description":"<p>About Scale AI</p>\n<p>At Scale AI, our mission is to accelerate the development of AI applications. This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation.</li>\n<li>Contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.</li>\n</ul>\n<p>Requirements</p>\n<ul>\n<li>Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow.</li>\n<li>A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.).</li>\n<li>At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.</li>\n</ul>\n<p>Nice to Have</p>\n<ul>\n<li>Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax.</li>\n<li>Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.</li>\n<li>Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.</li>\n<li>Familiarity with agentic reasoning methods such as STaR and PLANSEARCH</li>\n<li>Experience working with cloud technology stack (eg. 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We&#39;re looking for a Machine Learning Research Scientist/Engineer to join our team and help us shape the future of AI.</p>\n<p>This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). You will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale&#39;s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning.</p>\n<p>Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents</li>\n<li>Shape Scale&#39;s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning</li>\n<li>Contribute to impactful research on language model reasoning</li>\n<li>Collaborate with external researchers</li>\n<li>Work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions</li>\n</ul>\n<p>Requirements</p>\n<ul>\n<li>Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow</li>\n<li>A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.)</li>\n<li>At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning</li>\n<li>Strong written and verbal communication skills, along with the ability to work effectively across teams</li>\n</ul>\n<p>Nice to Have</p>\n<ul>\n<li>Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX</li>\n<li>Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents</li>\n<li>Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar</li>\n<li>Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH</li>\n<li>Proficiency in cloud-based ML development, with experience in AWS or GCP environments</li>\n</ul>\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>Salary Range</p>\n<p>$252,000-$315,000 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_fb1f459e-b3a","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/4605596005","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$252,000-$315,000 USD","x-skills-required":["PyTorch","JAX","TensorFlow","Large Language Models (LLMs)","Planning Algorithms","Agentic Reasoning","Data Generation","Model Interaction","Evaluation"],"x-skills-preferred":["Agent Frameworks","Cloud-Based ML Development","AWS","GCP","STaR","PLANSEARCH"],"datePosted":"2026-04-18T15:59:07.207Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; Seattle, WA; New York, NY"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, TensorFlow, Large Language Models (LLMs), Planning Algorithms, Agentic Reasoning, Data Generation, Model Interaction, Evaluation, Agent Frameworks, Cloud-Based ML Development, AWS, GCP, STaR, PLANSEARCH","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":252000,"maxValue":315000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8a3caae4-044"},"title":"Member of Technical Staff - Imagine Model","description":"<p>As a Member of Technical Staff on the Imagine Model Team, you will develop cutting-edge AI experiences beyond text, with a strong focus on enabling high-fidelity understanding and generation across image and video modalities. Responsibilities span data curation, modeling, training, inference serving, and product integration, covering both pretraining and post-training phases. 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You will build scalable multi-agent systems to validate agentic reasoning and behaviours, scale human expertise, and drive research into real-world agent reliability failures despite strong benchmarks, shipping production fixes.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Build and deploy multi-agent systems for agentic reasoning validation</li>\n<li>Develop pipelines to detect errors and scale human judgment</li>\n<li>Combine classical ML, LLMs, and multi-agent techniques for reliability</li>\n<li>Lead research into agent failure modes and ship fixes</li>\n<li>Use AI tools to speed prototyping and iteration</li>\n<li>Build data-driven evaluations and deploy rapid improvements</li>\n<li>Integrate systems into Scale&#39;s platform</li>\n</ul>\n<p><strong>Ideal Candidate</strong></p>\n<ul>\n<li>PhD or MSc in Computer Science, Mathematics, Statistics, or related field</li>\n<li>3+ years shipping scaled production ML systems</li>\n<li>Demonstrated real-world impact</li>\n<li>Mastery of PyTorch, TensorFlow, JAX, or scikit-learn</li>\n<li>Deep expertise in agentic LLMs and multi-agent systems</li>\n<li>Strong software engineering and microservices (AWS/GCP)</li>\n<li>Rapid, data-driven iteration</li>\n<li>Proficiency using AI tools to accelerate work</li>\n<li>Strong research depth with practical bias</li>\n<li>Excellent cross-functional communication</li>\n</ul>\n<p><strong>Nice to Have</strong></p>\n<ul>\n<li>Experience prototyping agent evaluation/reliability systems</li>\n<li>Human-in-the-loop or annotation pipeline work</li>\n<li>Open-source contributions in agents, evaluation, or alignment</li>\n<li>Publications on agent reliability (NeurIPS, ICML, ICLR)</li>\n</ul>\n<p><strong>Compensation</strong></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&#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><strong>About Us</strong></p>\n<p>At Scale, our mission is to develop reliable AI systems for the world&#39;s most important decisions. Our products provide the high-quality data and full-stack technologies that power the world&#39;s leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.</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_4f808d6c-a4e","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/4490301005","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$189,600-$237,000 USD","x-skills-required":["PyTorch","TensorFlow","JAX","scikit-learn","Agentic LLMs","Multi-agent systems","Software engineering","Microservices","Data-driven iteration","AI tools"],"x-skills-preferred":["Experience prototyping agent evaluation/reliability systems","Human-in-the-loop or annotation pipeline work","Open-source contributions in agents, evaluation, or alignment","Publications on agent reliability"],"datePosted":"2026-04-18T15:58:33.354Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, TensorFlow, JAX, scikit-learn, Agentic LLMs, Multi-agent systems, Software engineering, Microservices, Data-driven iteration, AI tools, Experience prototyping agent evaluation/reliability systems, Human-in-the-loop or annotation pipeline work, Open-source contributions in agents, evaluation, or alignment, Publications on agent reliability","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_7e28478b-c37"},"title":"Research, Audio Expertise","description":"<p>We&#39;re seeking a researcher to advance the frontier of audio capabilities. You&#39;ll explore how audio models enable more natural and efficient communication/collaboration, preserving more information and capturing user intent.</p>\n<p>This is a highly collaborative role. You&#39;ll work closely across pre-training, post-training, and product with world-class researchers, infrastructure engineers, and designers.</p>\n<p>As a researcher in this role, you&#39;ll be expected to:</p>\n<ul>\n<li>Own research projects on audio training, low-latency inference, and conversational responsiveness.</li>\n<li>Design and train large-scale models that natively support audio input and output.</li>\n<li>Investigate scaling behaviour such as how data, model size, and compute affect capability and efficiency.</li>\n<li>Build and maintain audio data pipelines, including preprocessing, filtering, segmentation, and alignment for training and evaluation.</li>\n<li>Collaborate with data and infrastructure teams to scale audio training efficiently across distributed systems.</li>\n<li>Publish and present research that moves the entire community forward.</li>\n</ul>\n<p>Share code, datasets, and insights that accelerate progress across industry and academia.</p>\n<p>This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports.</p>\n<p>It&#39;s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.</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_7e28478b-c37","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Thinking Machines Lab","sameAs":"https://thinkingmachines.ai/","logo":"https://logos.yubhub.co/thinkingmachines.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5002212008","x-work-arrangement":"onsite","x-experience-level":"mid|senior","x-job-type":"full-time","x-salary-range":"$350,000 - $475,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","JAX","Machine Learning","Deep Learning","Distributed Compute Environments"],"x-skills-preferred":["Probability","Statistics","Real-time Inference","Streaming Architectures","Optimization for Low Latency","Large-Scale Audio or Multimodal Models","Speech, Audio, Voice, or Similar Areas"],"datePosted":"2026-04-18T15:57:29.075Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, JAX, Machine Learning, Deep Learning, Distributed Compute Environments, Probability, Statistics, Real-time Inference, Streaming Architectures, Optimization for Low Latency, Large-Scale Audio or Multimodal Models, Speech, Audio, Voice, or Similar Areas","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":475000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_0a2ea62c-943"},"title":"Research Engineer, Infrastructure, RL Systems","description":"<p>We&#39;re looking for an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models through reinforcement learning.</p>\n<p>This role sits at the intersection of research and large-scale systems engineering: a builder who understands both the algorithms behind RL and the realities of distributed training and inference at scale. You&#39;ll wear many hats, from optimising rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers and infra teams to make reinforcement learning stable, fast, and production-ready.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design, build, and optimise the infrastructure that powers large-scale reinforcement learning and post-training workloads.</li>\n</ul>\n<ul>\n<li>Improve the reliability and scalability of RL training pipeline, distributed RL workloads, and training throughput.</li>\n</ul>\n<ul>\n<li>Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.</li>\n</ul>\n<ul>\n<li>Collaborate with researchers to translate algorithmic ideas into production-grade training pipelines.</li>\n</ul>\n<ul>\n<li>Build evaluation and benchmarking infrastructure that measures model progress on helpfulness, safety, and factuality.</li>\n</ul>\n<ul>\n<li>Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.</li>\n</ul>\n<p>We&#39;re looking for someone with strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases. 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While we can&#39;t guarantee success for every candidate or role, if you&#39;re the right fit, we&#39;re committed to working through the visa process together.</li>\n<li>Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.</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_07a3c83e-51e","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Thinking Machines Lab","sameAs":"https://thinkingmachines.ai/","logo":"https://logos.yubhub.co/thinkingmachines.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5013937008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $475,000 USD","x-skills-required":["Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar","Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures","Thriving in a highly collaborative environment involving many, different cross-functional partners and subject matter experts","Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases in areas such as floating-point numerics, low-precision arithmetic, and distributed systems","Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM"],"x-skills-preferred":["Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs","Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA","Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models","Experience training and supporting large-scale AI models","Track record of improving research productivity through infrastructure design or process improvements"],"datePosted":"2026-04-18T15:56:14.922Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar, Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures, Thriving in a highly collaborative environment involving many, different cross-functional partners and subject matter experts, Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases in areas such as floating-point numerics, low-precision arithmetic, and distributed systems, Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM, Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs, Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA, Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models, Experience training and supporting large-scale AI models, Track record of improving research productivity through infrastructure design or process improvements","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":475000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_0e93287d-e38"},"title":"Applied Research Engineer","description":"<p>Shape the Future of AI</p>\n<p>At Labelbox, we&#39;re building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we&#39;ve been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.</p>\n<p>As an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), etc. You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process.</p>\n<p>Your Impact</p>\n<ul>\n<li>Advance the field of AI alignment by developing cutting-edge methods, such as RLHF and novel approaches, that ensure AI systems reflect human preferences more accurately.</li>\n</ul>\n<ul>\n<li>Improve the quality of human-in-the-loop data by designing and deploying rigorous measurement and enhancement systems, leading to more reliable AI training.</li>\n</ul>\n<ul>\n<li>Increase efficiency and effectiveness in AI-assisted data labeling by creating tools that leverage active learning and adaptive sampling, reducing manual effort while improving accuracy.</li>\n</ul>\n<ul>\n<li>Shape the next generation of AI models by investigating how different types of human feedback (e.g., demonstrations, preferences, critiques) impact model performance and alignment.</li>\n</ul>\n<ul>\n<li>Optimize human feedback collection by developing novel algorithms that enhance how AI learns from human input, improving model adaptability and responsiveness.</li>\n</ul>\n<ul>\n<li>Bridge research and real-world application by integrating breakthroughs into Labelbox’s product suite, making human-AI alignment techniques scalable and impactful for users.</li>\n</ul>\n<ul>\n<li>Drive industry innovation by engaging with customers and the broader AI community to understand evolving data needs and share best practices for training frontier models.</li>\n</ul>\n<ul>\n<li>Contribute to the AI research ecosystem by publishing in top-tier journals, presenting at leading conferences, and influencing the future of human-centric AI.</li>\n</ul>\n<ul>\n<li>Stay ahead of AI advancements by continuously exploring new frontiers in human-AI collaboration, human data quality, and AI alignment, keeping Labelbox at the cutting edge.</li>\n</ul>\n<ul>\n<li>Establish Labelbox as a thought leader in AI by creating technical documentation, blog posts, and educational content that shape the industry&#39;s approach to human-centric AI development.</li>\n</ul>\n<p>What You Bring</p>\n<ul>\n<li>A strong foundation in AI and machine learning, backed by a Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related field.</li>\n</ul>\n<ul>\n<li>Proven experience (3+ years) in solving complex ML challenges and delivering impactful solutions that improve real-world AI applications.</li>\n</ul>\n<ul>\n<li>Expertise in designing and implementing data quality measurement and refinement systems that directly enhance model performance and reliability.</li>\n</ul>\n<ul>\n<li>A deep understanding of frontier AI models,such as large language models and multimodal models,and the human data strategies needed to optimize them.</li>\n</ul>\n<ul>\n<li>Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow to prototype and develop cutting-edge solutions.</li>\n</ul>\n<ul>\n<li>A track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community.</li>\n</ul>\n<ul>\n<li>The ability to bridge research and application by interpreting new findings and rapidly translating them into functional prototypes.</li>\n</ul>\n<ul>\n<li>Strong analytical and problem-solving skills that enable you to tackle ambiguous AI challenges with structured, data-driven approaches.</li>\n</ul>\n<ul>\n<li>Exceptional communication and collaboration skills, allowing you to work effectively across multidisciplinary teams and with external stakeholders.</li>\n</ul>\n<p>Labelbox Applied Research</p>\n<p>At Labelbox Applied Research, we&#39;re committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.</p>\n<p>We foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.</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_0e93287d-e38","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Labelbox","sameAs":"https://www.labelbox.com/","logo":"https://logos.yubhub.co/labelbox.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/labelbox/jobs/4640965007","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$250,000-$300,000 USD","x-skills-required":["AI","Machine Learning","Deep Learning","Python","PyTorch","JAX","TensorFlow","Data Quality Measurement","Refinement Systems","Human-AI Interaction","Frontier AI Models","Large Language Models","Multimodal Models"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:55:40.503Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco Bay Area"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"AI, Machine Learning, Deep Learning, Python, PyTorch, JAX, TensorFlow, Data Quality Measurement, Refinement Systems, Human-AI Interaction, Frontier AI Models, Large Language Models, Multimodal Models","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":300000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d6d2907d-177"},"title":"Research Engineer, Post-Training for Code Security Analysis","description":"<p>About Us</p>\n<p>Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.</p>\n<p><strong>The Role</strong></p>\n<p>In this role, you&#39;ll work with a team of elite researchers and engineers to design and implement post-training strategies that enhance Gemini’s capabilities in code security analysis. You will bring contributions to our ML innovation, post-training refinement (SFT/RLHF), advanced evaluation, and data generation to ensure our models can reliably perform safe and powerful code security analysis.</p>\n<p><strong>Key responsibilities:</strong></p>\n<ul>\n<li>Design and Implement advanced post-training algorithms (SFT, RLHF, RLAIF) to optimize Gemini for code security tasks and secure coding practices.</li>\n</ul>\n<ul>\n<li>Diagnose and interpret training outcomes (regressions in coding ability, gains in security reasoning), and propose solutions to improve model capabilities.</li>\n</ul>\n<ul>\n<li>Actively monitor and evolve the system&#39;s performance through metric design.</li>\n</ul>\n<ul>\n<li>Develop reliable automated evaluation pipelines for code security that are strongly correlated with human security expert judgment.</li>\n</ul>\n<ul>\n<li>Construct complex benchmarks to probe the limits of the model’s ability to reason about control flow, memory safety, and software weakness.</li>\n</ul>\n<p><strong>About You</strong></p>\n<p>We are seeking individuals who excel in fast-pacing environments and are eager to contribute to the advancement of AI. We highly value the ability to invent novel solutions to complex problems, embracing a can-do and fail-fast mindset. We are looking for someone who genuinely believes in the future of AI and is committed to devoting their energy in this field.</p>\n<p>In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:</p>\n<ul>\n<li>BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry</li>\n</ul>\n<ul>\n<li>Deep understanding of statistics is strongly preferred</li>\n</ul>\n<ul>\n<li>Experiences in fine-tuning and adaptation of LLMs (e.g. advanced prompting, supervised fine-tuning, RLHF)</li>\n</ul>\n<ul>\n<li>Strong knowledge of systems design and data structures</li>\n</ul>\n<ul>\n<li>Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks</li>\n</ul>\n<ul>\n<li>Recent experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models</li>\n</ul>\n<ul>\n<li>A passion for Artificial Intelligence.</li>\n</ul>\n<ul>\n<li>Excellent communication skills and proven interpersonal skills, with a track record of effective collaboration with cross-functional teams</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_d6d2907d-177","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7397549","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry","Deep understanding of statistics","Experiences in fine-tuning and adaptation of LLMs","Strong knowledge of systems design and data structures","Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks"],"x-skills-preferred":["A passion for Artificial Intelligence","Excellent communication skills and proven interpersonal skills"],"datePosted":"2026-04-18T15:54:55.309Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry, Deep understanding of statistics, Experiences in fine-tuning and adaptation of LLMs, Strong knowledge of systems design and data structures, Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks, A passion for Artificial Intelligence, Excellent communication skills and proven interpersonal skills"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_cba88898-896"},"title":"Research Engineer, Infrastructure, Kernels","description":"<p>We&#39;re looking for an infrastructure research engineer to design, optimize, and maintain the compute foundations that power large-scale language model training. 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You&#39;ll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.</li>\n<li>Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.</li>\n<li>Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.</li>\n<li>Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.</li>\n<li>Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.</li>\n<li>Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.</li>\n</ul>\n<p><strong>Skills and Qualifications</strong></p>\n<p>Minimum qualifications:</p>\n<ul>\n<li>Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.</li>\n<li>Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases</li>\n<li>Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.</li>\n<li>Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.</li>\n<li>A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.</li>\n<li>Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.</li>\n<li>Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.</li>\n</ul>\n<p>Preferred qualifications:</p>\n<ul>\n<li>Experience training or supporting large-scale language models with tens of billions of parameters or more.</li>\n<li>Track record of improving research productivity through infrastructure design or process improvements.</li>\n<li>Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.</li>\n<li>Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.</li>\n<li>Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).</li>\n<li>Contributions to open-source GPU, ML systems, or compiler optimization projects.</li>\n<li>Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.</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_cba88898-896","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Thinking Machines Lab","sameAs":"https://thinkingmachines.ai/","logo":"https://logos.yubhub.co/thinkingmachines.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5013934008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - 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As a Research Engineer, you&#39;ll advance our models&#39; ability to safely write correct, fast code for accelerators.</p>\n<p>You&#39;ll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:</p>\n<ul>\n<li>Invent, design and implement RL environments and evaluations.</li>\n<li>Conduct experiments and shape our research roadmap.</li>\n<li>Deliver your work into training runs.</li>\n<li>Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.</li>\n</ul>\n<p>We&#39;re looking for someone with expertise in accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch), and experience with balancing research exploration with engineering implementation.</p>\n<p>Strong candidates may also have experience with reinforcement learning, porting ML workloads between different types of accelerators, and familiarity with LLM training methodologies.</p>\n<p>The annual compensation range for this role is $350,000-$850,000 USD.</p>\n<p>Please note that we&#39;re an extremely collaborative group, and we value communication skills. The easiest way to understand our research directions is to read our recent research.</p>\n<p>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.</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_c9ab5cbc-dd6","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/5160330008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["accelerator performance","ML framework programming","reinforcement learning","RL environments and evaluations","experiments and research roadmap","training runs","collaboration with researchers and engineers"],"x-skills-preferred":["CUDA","ROCm","Triton","Pallas","JAX","PyTorch","LLM training methodologies"],"datePosted":"2026-04-18T15:54:02.762Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"accelerator performance, ML framework programming, reinforcement learning, RL environments and evaluations, experiments and research roadmap, training runs, collaboration with researchers and engineers, CUDA, ROCm, Triton, Pallas, JAX, PyTorch, LLM training methodologies","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_dc17980d-461"},"title":"Research Engineer, Interpretability","description":"<p>JOB TITLE: Research Engineer, Interpretability \\n LOCATION: San Francisco, CA \\n DEPARTMENT: AI Research &amp; Engineering \\n \\n JOB DESCRIPTION: \\n \\n 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; \\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; can quickly learn and put that knowledge to work, e.g. diving into new layers of the stack to find bottlenecks \\n - Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions \\n - Prefer fast-moving collaborative projects to extensive solo efforts \\n - Are curious about interpretability research and its role in AI safety (though no research experience is required!) \\n - Care about the societal impacts and ethics of your work \\n - Are comfortable working closely with researchers, translating research needs into engineering solutions. \\n \\n STRONG CANDIDATES MAY ALSO HAVE EXPERIENCE WITH: \\n - Optimizing the performance of large-scale distributed systems \\n - Language modeling fundamentals with transformers \\n - High Performance LLM optimization: memory management, compute efficiency, parallelism strategies, inference throughput optimization \\n - Working hands-on in a mainstream ML stack - PyTorch/CUDA on GPUs or JAX/XLA on TPUs \\n - Collaborating closely with researchers and building tooling to support research teams; or directly performed research with complex engineering challenges \\n \\n REPRESENTATIVE PROJECTS: \\n - Building Garcon, a tool that allows researchers to easily instrument LLMs to extract internal activations \\n - Designing and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them \\n - Profiling and optimizing ML training jobs, including multi-GPU parallelism and memory optimization \\n - Building a steered inference system that applies targeted interventions to model internals at scale (conceptually similar to Golden Gate Claude but for safety research) \\n \\n ROLE SPECIFIC LOCATION POLICY: \\n - 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. \\n \\n The annual compensation range for this role is listed below. \\n 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. \\n 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_dc17980d-461","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","High Performance LLM optimization","memory management","compute efficiency","parallelism strategies","inference throughput optimization"],"x-skills-preferred":["large-scale distributed systems","language modeling fundamentals","transformers","collaborating closely with researchers","building tooling to support research teams"],"datePosted":"2026-04-18T15:53:01.682Z","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, High Performance LLM optimization, memory management, compute efficiency, parallelism strategies, inference throughput optimization, large-scale distributed systems, language modeling fundamentals, transformers, collaborating closely with researchers, 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_4ced2159-802"},"title":"Research, Vision Expertise","description":"<p>Thinking Machines Lab is seeking a researcher to join their team in San Francisco. The successful candidate will work on advancing the science of visual perception and multimodal learning. They will design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world.</p>\n<p>The ideal candidate will have expertise in multimodality and experience running large-scale experiments. They will be comfortable contributing to complex engineering systems and have a strong grasp of probability, statistics, and machine learning fundamentals.</p>\n<p>This is an evergreen role, meaning that the position is open on an ongoing basis. The company receives many applications, and there may not always be an immediate role that aligns perfectly with the candidate&#39;s experience and skills. However, they encourage candidates to apply and continuously review applications.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own research projects on training and performance analysis of multimodal AI models.</li>\n<li>Curate and build large-scale datasets and evaluation benchmarks to advance vision capabilities.</li>\n<li>Work with data infrastructure engineers, pretraining researchers and engineers, and product teams to create frontier multimodal models and the products that leverage them.</li>\n<li>Publish and present research that moves the entire community forward.</li>\n</ul>\n<p>Skills and Qualifications:</p>\n<ul>\n<li>Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.</li>\n<li>Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.</li>\n<li>Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).</li>\n<li>Comfortable with debugging distributed training and writing code that scales.</li>\n<li>Bachelor&#39;s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.</li>\n</ul>\n<p>Preferred qualifications include research or engineering contributions in visual reasoning, spatial understanding, or multimodal architecture design, experience developing evaluation frameworks for multimodal tasks, publications or open-source contributions in vision-language modeling, video understanding, or multimodal AI, and a strong grasp of probability, statistics, and ML fundamentals.</p>\n<p>Logistics:</p>\n<ul>\n<li>Location: San Francisco, California.</li>\n<li>Compensation: $350,000 - $475,000 USD per year, depending on background, skills, and experience.</li>\n<li>Visa sponsorship: Yes.</li>\n<li>Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.</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_4ced2159-802","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Thinking Machines Lab","sameAs":"https://thinkingmachines.ai/","logo":"https://logos.yubhub.co/thinkingmachines.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5002288008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $475,000 USD per year","x-skills-required":["Python","Deep learning framework (e.g., PyTorch, TensorFlow, or JAX)","Machine learning fundamentals","Large-scale training","Distributed compute environments"],"x-skills-preferred":["Visual reasoning","Spatial understanding","Multimodal architecture design","Evaluation frameworks for multimodal tasks","Vision-language modeling","Video understanding","Multimodal AI"],"datePosted":"2026-04-18T15:52:43.848Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep learning framework (e.g., PyTorch, TensorFlow, or JAX), Machine learning fundamentals, Large-scale training, Distributed compute environments, Visual reasoning, Spatial understanding, Multimodal architecture design, Evaluation frameworks for multimodal tasks, Vision-language modeling, Video understanding, Multimodal AI","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":475000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ed5725bb-311"},"title":"Applied Research Engineer, Agents","description":"<p>Shape the Future of AI</p>\n<p>At Labelbox, we&#39;re building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we&#39;ve been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.</p>\n<p>As an Applied Research Engineer at Labelbox, you&#39;ll sit at the junction of advanced AI research and real product impact, with a focus on the data that makes modern agents work,browser interactions, SWE/code traces, GUI sessions, and multi-turn workflows. You&#39;ll drive the data landscape required to advance capable, adaptable agents and help shape Labelbox&#39;s strategy for collecting, synthesizing, and evaluating it.</p>\n<p>Create frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities.</p>\n<p>Design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.</p>\n<p>Develop data pipelines from diverse sources like code repositories, web browsers, and computer systems.</p>\n<p>Implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models.</p>\n<p>Engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices.</p>\n<p>Collaborate closely with frontier AI lab customers to understand requirements and guide model development.</p>\n<p>Publish research findings in academic journals, conferences, and blog posts.</p>\n<p>What You Bring</p>\n<p>Ph.D. or Master&#39;s degree in Computer Science, Machine Learning, AI, or related field.</p>\n<p>At least 3 years of experience addressing sophisticated ML problems with successful delivery to customers.</p>\n<p>Experience building and training autonomous agents,tool use, structured outputs, multi-step planning,across browsers/GUI, codebases, and databases using SFT and RL.</p>\n<p>Constructed and evaluated agentic benchmarks (e.g. SWE-bench, WebArena, τ-bench, OSWorld) and reliability/efficiency suites (e.g. WABER).</p>\n<p>Adept at interpreting research literature and quickly turning new ideas into prototypes.</p>\n<p>Deep understanding of frontier models (autoregressive, diffusion), post-training (SFT, RLVR, RLAIF, RLHF, et al.), and their human data requirements.</p>\n<p>Proficient in Python, data science libraries and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).</p>\n<p>Strong analytical and problem-solving abilities in ambiguous situations.</p>\n<p>Excellent communication skills.</p>\n<p>Track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).</p>\n<p>Labelbox Applied Research</p>\n<p>At Labelbox Applied Research, we&#39;re committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.</p>\n<p>Life at Labelbox</p>\n<p>Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland</p>\n<p>Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility</p>\n<p>Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making</p>\n<p>Growth: Career advancement opportunities directly tied to your impact</p>\n<p>Vision: Be part of building the foundation for humanity&#39;s most transformative technology</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_ed5725bb-311","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Labelbox","sameAs":"https://www.labelbox.com/","logo":"https://logos.yubhub.co/labelbox.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/labelbox/jobs/4829775007","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$250,000-$300,000 USD","x-skills-required":["Python","data science libraries","deep learning frameworks","PyTorch","JAX","TensorFlow","supervised fine-tuning","reinforcement learning","agent libraries","benchmarks","proprietary datasets","human-AI interaction","AI ethics"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:52:38.777Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco Bay Area"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, data science libraries, deep learning frameworks, PyTorch, JAX, TensorFlow, supervised fine-tuning, reinforcement learning, agent libraries, benchmarks, proprietary datasets, human-AI interaction, AI ethics","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":300000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_46c30960-10d"},"title":"AI Applied Scientist","description":"<p>We&#39;re looking for applied scientists with a Machine Learning and Artificial Intelligence background to build AI technologies and make Figma products more magical. You will be driving fundamental and applied research in this area. You will be combining industry best practices and a first-principles approach to design and build AI/ML models and systems to improve Figma&#39;s products.</p>\n<p>What you&#39;ll do at Figma:</p>\n<ul>\n<li>Drive fundamental and applied research in AI.</li>\n<li>Explore the boundaries of what is possible with the current technology set to build best in class models for Figma&#39;s domains.</li>\n<li>Combine industry best practices and a first-principles approach to build cutting edge Generative AI models, using techniques like Supervised Finetuning (SFT), Reinforcement Learning (RL), prompt improvements and synthetic data generation.</li>\n<li>Work in concert with product and infrastructure engineers to improve Figma&#39;s products through AI powered features.</li>\n<li>Collaborate closely with product managers and engineers to transform user feedback into requirements for AI systems.</li>\n<li>Build evaluation systems to measure and improve quality of AI features in Figma products.</li>\n</ul>\n<p>We&#39;d love to hear from you if you have:</p>\n<ul>\n<li>Extensive experience in building generative AI features through prompt engineering, and fine tuning models in production environments.</li>\n<li>Experience working on deep learning and generative AI frameworks like PyTorch, JAX, HuggingFace etc.</li>\n<li>Experience training LLMs with Reinforcement Learning techniques such as preference-based RL (DPO, PPO) and/or RL with verifiable rewards (RLVR) such as GRPO/DAPO.</li>\n<li>4+ years in Generative AI, and 6+ years of experience in one or more of the following areas: machine learning, natural language processing/understanding, computer vision.</li>\n<li>Strong software engineering skills with 5+ years of experience in programming languages (Python, C++, Java or R).</li>\n<li>Experience communicating and working across functions to drive solutions.</li>\n</ul>\n<p>While not required, It’s an added plus if you also have:</p>\n<ul>\n<li>Proven track record of planning multi-year roadmap in which shorter-term projects ladder to the long-term vision.</li>\n<li>Experience in mentoring/influencing senior engineers across organizations.</li>\n<li>Expertise working on large scale and distributed AI training.</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_46c30960-10d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Figma","sameAs":"https://www.figma.com/","logo":"https://logos.yubhub.co/figma.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/figma/jobs/5707966004","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$153,000-$376,000 USD","x-skills-required":["Generative AI","Machine Learning","Artificial Intelligence","Deep Learning","PyTorch","JAX","HuggingFace","Reinforcement Learning","Natural Language Processing","Computer Vision","Python","C++","Java","R"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:49:15.355Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA • New York, NY • United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Generative AI, Machine Learning, Artificial Intelligence, Deep Learning, PyTorch, JAX, HuggingFace, Reinforcement Learning, Natural Language Processing, Computer Vision, Python, C++, Java, R","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":153000,"maxValue":376000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_b210c75f-2d9"},"title":"Research Engineer, Machine Learning (Reinforcement Learning)","description":"<p>As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction.</p>\n<p>You will work on fundamental research in reinforcement learning, creating &#39;agentic&#39; models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.</li>\n<li>Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.</li>\n<li>Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.</li>\n<li>Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Are proficient in Python and async/concurrent programming with frameworks like Trio.</li>\n<li>Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX).</li>\n<li>Have industry experience in machine learning research.</li>\n<li>Can balance research exploration with engineering implementation.</li>\n<li>Enjoy pair programming (we love to pair!).</li>\n<li>Care about code quality, testing, and performance.</li>\n<li>Have strong systems design and communication skills.</li>\n<li>Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems.</li>\n</ul>\n<p>Strong candidates may have:</p>\n<ul>\n<li>Familiarity with LLM architectures and training methodologies.</li>\n<li>Experience with reinforcement learning techniques and environments.</li>\n<li>Experience with virtualization and sandboxed code execution environments.</li>\n<li>Experience with Kubernetes.</li>\n<li>Experience with distributed systems or high-performance computing.</li>\n<li>Experience with Rust and/or C++.</li>\n</ul>\n<p>Strong candidates need not have:</p>\n<ul>\n<li>Formal certifications or education credentials.</li>\n<li>Academic research experience or publication history.</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_b210c75f-2d9","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/5115935008","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"£260,000-£630,000 GBP","x-skills-required":["Python","async/concurrent programming","Trio","machine learning frameworks","PyTorch","TensorFlow","JAX","reinforcement learning techniques","environments","virtualization","sandboxed code execution environments","Kubernetes","distributed systems","high-performance computing","Rust","C++"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:46:19.569Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, async/concurrent programming, Trio, machine learning frameworks, PyTorch, TensorFlow, JAX, reinforcement learning techniques, environments, virtualization, sandboxed code execution environments, Kubernetes, distributed systems, high-performance computing, Rust, C++","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"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_bd9625d9-99b"},"title":"ML Infrastructure Engineer, Safeguards","description":"<p>We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you&#39;ll build and scale the critical infrastructure that powers our AI safety systems.</p>\n<p>As part of the Safeguards team, you&#39;ll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem</li>\n<li>Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications</li>\n<li>Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems</li>\n<li>Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards</li>\n<li>Implement automated testing, deployment, and rollback systems for ML models in production safety applications</li>\n<li>Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs</li>\n<li>Contribute to the development of internal tools and frameworks that accelerate safety research and deployment</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment</li>\n<li>Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX</li>\n<li>Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)</li>\n<li>Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads</li>\n<li>Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)</li>\n<li>Are results-oriented, with a bias towards reliability and impact in safety-critical systems</li>\n<li>Enjoy collaborating with researchers and translating cutting-edge research into production systems</li>\n<li>Care deeply about AI safety and the societal impacts of your work</li>\n</ul>\n<p>Strong candidates may have experience with:</p>\n<ul>\n<li>Working with large language models and modern transformer architectures</li>\n<li>Implementing A/B testing frameworks and experimentation infrastructure for ML systems</li>\n<li>Developing monitoring and alerting systems for ML model performance and data drift</li>\n<li>Building automated labeling systems and human-in-the-loop workflows</li>\n<li>Experience in trust &amp; safety, fraud prevention, or content moderation domains</li>\n<li>Knowledge of privacy-preserving ML techniques and compliance requirements</li>\n<li>Contributing to open-source ML infrastructure projects</li>\n</ul>\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.</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_bd9625d9-99b","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/4778843008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000-$405,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","JAX","Cloud platforms (AWS, GCP)","Container orchestration (Kubernetes)","Distributed systems principles","Data engineering tools (Spark, Airflow, streaming systems)"],"x-skills-preferred":["Large language models and modern transformer architectures","A/B testing frameworks and experimentation infrastructure for ML systems","Monitoring and alerting systems for ML model performance and data drift","Automated labeling systems and human-in-the-loop workflows","Trust & safety, fraud prevention, or content moderation domains","Privacy-preserving ML techniques and compliance requirements","Open-source ML infrastructure projects"],"datePosted":"2026-04-18T15:44:06.907Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, JAX, Cloud platforms (AWS, GCP), Container orchestration (Kubernetes), Distributed systems principles, Data engineering tools (Spark, Airflow, streaming systems), Large language models and modern transformer architectures, A/B testing frameworks and experimentation infrastructure for ML systems, Monitoring and alerting systems for ML model performance and data drift, Automated labeling systems and human-in-the-loop workflows, Trust & safety, fraud prevention, or content moderation domains, Privacy-preserving ML techniques and compliance requirements, Open-source ML infrastructure projects","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9ecceef8-349"},"title":"Research Engineer/Research Scientist, Audio","description":"<p>We are seeking a Research Engineer/Research Scientist to join our Audio team. As a member of this team, you will work across the full stack of audio ML, developing audio codecs and representations, sourcing and synthesizing high-quality audio data, training large-scale speech language models and large audio diffusion models, and developing novel architectures for incorporating continuous signals into LLMs.</p>\n<p>Our team focuses primarily but not exclusively on speech, building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities. The team works closely with many collaborators across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high-impact real-world deployments.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Develop and train audio models, including conversational speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, and generative audio models</li>\n<li>Work across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization</li>\n<li>Collaborate with teams across the company to develop and deploy audio technologies</li>\n<li>Communicate clearly and effectively with colleagues and stakeholders</li>\n</ul>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>Large language model pretraining and finetuning</li>\n<li>Training diffusion models for image and audio generation</li>\n<li>Reinforcement learning for large language models and diffusion models</li>\n<li>End-to-end system optimization, from performance benchmarking to kernel optimization</li>\n<li>GPUs, Kubernetes, PyTorch, or distributed training infrastructure</li>\n</ul>\n<p>Representative projects:</p>\n<ul>\n<li>Training state-of-the-art neural audio codecs for 48 kHz stereo audio</li>\n<li>Developing novel algorithms for diffusion pretraining and reinforcement learning</li>\n<li>Scaling audio datasets to millions of hours of high-quality audio</li>\n<li>Creating robust evaluation methodologies for hard-to-measure qualities such as naturalness or expressiveness</li>\n<li>Studying training dynamics of mixed audio-text language models</li>\n<li>Optimizing latency and inference throughput for deployed streaming audio systems</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_9ecceef8-349","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/5074815008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$500,000 USD","x-skills-required":["JAX","PyTorch","large-scale distributed training","signal processing fundamentals","speech language models","audio diffusion models","continuous signals","LLMs"],"x-skills-preferred":["large language model pretraining","diffusion models","reinforcement learning","end-to-end system optimization","GPUs","Kubernetes","distributed training infrastructure"],"datePosted":"2026-04-18T15:42:59.425Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"JAX, PyTorch, large-scale distributed training, signal processing fundamentals, speech language models, audio diffusion models, continuous signals, LLMs, large language model pretraining, diffusion models, reinforcement learning, end-to-end system optimization, GPUs, Kubernetes, distributed training infrastructure","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":500000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_dc6154f8-cff"},"title":"Research Engineer, Pretraining Scaling - London","description":"<p>About Anthropic\\n\\nAnthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems.\\n\\nAbout the Role:\\n\\nAs a Research Engineer on Anthropic&#39;s ML Performance and Scaling team, you&#39;ll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\\n\\nResponsibilities:\\n\\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\\n- Add new capabilities to the training codebase, such as long context support or novel architectures\\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\\n- Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned\\n\\nYou May Be a Good Fit If You:\\n\\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\\n- Genuinely enjoy both research and engineering work,you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\\n- Are passionate about the work itself and want to refine your craft as a research engineer\\n- Care about the societal impacts of AI and responsible scaling\\n\\nStrong Candidates May Also Have:\\n\\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\\n- Published research on model training, scaling laws, or ML systems\\n- Experience with production ML systems, observability tools, or evaluation infrastructure\\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\\n\\nWhat Makes This Role Unique:\\n\\nThis is not a typical research engineering role. The work is highly operational,you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\\n\\nHowever, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.\\n\\nWe&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI,and you&#39;re excited about the full reality of what that entails,we&#39;d love to hear from you.\\n\\nLocation:\\n\\nThis role requires working in-office 5 days per week in London.\\n\\nDeadline to apply:\\n\\nNone. 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:\\n\\n£260,000-£630,000 GBP\\n\\nLogistics\\n\\nMinimum education:\\n\\nBachelor’s degree or an equivalent combination of education, training, and/or experience\\n\\nRequired field of study:\\n\\nA field relevant to the role as demonstrated through coursework, training, or professional experience\\n\\nMinimum years of experience:\\n\\nYears of experience required will correlate with the internal job level requirements for the position\\n\\nLocation-based hybrid policy:\\n\\nCurrently, 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:\\n\\nWe 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 h</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_dc6154f8-cff","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/4938436008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000-£630,000 GBP","x-skills-required":["JAX","TPU","PyTorch","large-scale distributed systems","model operations","performance optimization","observability","reliability","debugging","complex issues","hardware errors","networking","training dynamics","evaluation infrastructure","experiments","training efficiency","step time","uptime","model performance","production logging","monitoring dashboards","codebase","long context support","novel architectures","collaboration","institutional knowledge","documentation","debugging approaches","lessons learned"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:42:55.023Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"JAX, TPU, PyTorch, large-scale distributed systems, model operations, performance optimization, observability, reliability, debugging, complex issues, hardware errors, networking, training dynamics, evaluation infrastructure, experiments, training efficiency, step time, uptime, model performance, production logging, monitoring dashboards, codebase, long context support, novel architectures, collaboration, institutional knowledge, documentation, debugging approaches, lessons learned","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_f49203e0-6c6"},"title":"Research Engineer, Science of Scaling","description":"<p>We are seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Conduct research into the science of converting compute into intelligence</li>\n<li>Independently lead small research projects while collaborating with team members on larger initiatives</li>\n<li>Design, run, and analyze scientific experiments to advance our understanding of large language models</li>\n<li>Optimize training infrastructure to improve efficiency and reliability</li>\n<li>Develop dev tooling to enhance team productivity</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have significant software engineering experience and a proven track record of building complex systems</li>\n<li>Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field</li>\n<li>Are proficient in Python and experienced with deep learning frameworks</li>\n<li>Are results-oriented with a bias towards flexibility and impact</li>\n<li>Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team</li>\n<li>View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximize impact</li>\n<li>Care about the societal impacts of your work and have ambitious goals for AI safety and general progress</li>\n</ul>\n<p>Strong candidates may have:</p>\n<ul>\n<li>Experience with JAX</li>\n<li>Experience with reinforcement learning</li>\n<li>Experience working on high-performance, large-scale ML systems</li>\n<li>Familiarity with accelerators, Kubernetes, and OS internals</li>\n<li>Experience with language modeling using transformer architectures</li>\n<li>Background in large-scale ETL processes</li>\n<li>Experience with distributed training at scale (thousands of accelerators)</li>\n</ul>\n<p>Strong candidates need not have:</p>\n<ul>\n<li>Experience in all of the above areas , we value breadth of interest and willingness to learn over checking every box</li>\n<li>Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well</li>\n<li>An advanced degree , exceptional engineers with strong research instincts are equally encouraged to apply</li>\n</ul>\n<p>The annual compensation range for this role is £260,000-£630,000 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_f49203e0-6c6","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/5126127008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000-£630,000 GBP","x-skills-required":["Python","Deep learning frameworks","Software engineering","Machine learning","Advanced degree in Computer Science or related field"],"x-skills-preferred":["JAX","Reinforcement learning","High-performance, large-scale ML systems","Accelerators","Kubernetes","OS internals","Language modeling using transformer architectures","Large-scale ETL processes","Distributed training at scale"],"datePosted":"2026-04-18T15:42:40.887Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep learning frameworks, Software engineering, Machine learning, Advanced degree in Computer Science or related field, JAX, Reinforcement learning, High-performance, large-scale ML systems, Accelerators, Kubernetes, OS internals, Language modeling using transformer architectures, Large-scale ETL processes, Distributed training at scale","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6960fd5f-0e8"},"title":"Research Engineer, Pretraining Scaling","description":"<p><strong>About the Role:\\n\\nAs a Research Engineer on Anthropic&#39;s ML Performance and Scaling team, you&#39;ll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\\n\\n## Responsibilities:\\n\\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\\n- Add new capabilities to the training codebase, such as long context support or novel architectures\\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\\n- Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned\\n\\n## You May Be a Good Fit If You:\\n\\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\\n- Genuinely enjoy both research and engineering work,you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\\n- Are passionate about the work itself and want to refine your craft as a research engineer\\n- Care about the societal impacts of AI and responsible scaling\\n\\n## Strong Candidates May Also Have:\\n\\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\\n- Published research on model training, scaling laws, or ML systems\\n- Experience with production ML systems, observability tools, or evaluation infrastructure\\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\\n\\n## What Makes This Role Unique:\\n\\nThis is not a typical research engineering role. The work is highly operational,you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\\n\\nHowever, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.\\n\\nWe&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI,and you&#39;re excited about the full reality of what that entails,we&#39;d love to hear from you.\\n\\nLocation: This role requires working in-office 5 days per week in San Francisco.\\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: $350,000-$850,000 USD\\n\\n## Logistics\\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\\n## How 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. 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As a Research Engineer, you&#39;ll advance our models&#39; ability to safely write correct, fast code for accelerators.</p>\n<p>You&#39;ll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:</p>\n<ul>\n<li>Invent, design and implement RL environments and evaluations.</li>\n<li>Conduct experiments and shape our research roadmap.</li>\n<li>Deliver your work into training runs.</li>\n<li>Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).</li>\n<li>Have worked across the stack – kernels, model code, distributed systems.</li>\n<li>Know how to balance research exploration with engineering implementation.</li>\n<li>Are passionate about AI&#39;s potential and committed to developing safe and beneficial systems.</li>\n</ul>\n<p>Strong candidates may also have:</p>\n<ul>\n<li>Experience with reinforcement learning.</li>\n<li>Experience porting ML workloads between different types of accelerators.</li>\n<li>Familiarity with LLM training methodologies.</li>\n</ul>\n<p>The annual compensation range for this role is $350,000-$850,000 USD.</p>\n<p>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>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.</p>\n<p>We kitchen is a public benefit corporation headquartered in San Francisco. 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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.</p>\n<p>Responsibilities:</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>You may be a good fit if you:</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 candidates may also have:</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>The annual compensation range for this role is $350,000-$850,000 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_18ae1499-b22","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/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":["large-scale distributed systems","containerization technologies (Docker, Kubernetes)","performance optimization techniques","system architectures for high-throughput ML workloads","data pipelines","distributed storage systems","ML frameworks (PyTorch, JAX, etc.)","GPU/TPU architectures","cloud platforms (AWS, GCP)","VM and container orchestration","workflow orchestration tools","experiment management systems","reinforcement learning","large scale data pipelines (Beam, Spark, Dask, …)"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:41:42.408Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"large-scale distributed systems, containerization technologies (Docker, Kubernetes), performance optimization techniques, system architectures for high-throughput ML workloads, data pipelines, distributed storage systems, ML frameworks (PyTorch, JAX, etc.), GPU/TPU architectures, cloud platforms (AWS, GCP), VM and container orchestration, workflow orchestration tools, experiment management systems, reinforcement learning, large scale data pipelines (Beam, Spark, Dask, …)","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_1ee5ad51-8f0"},"title":"SWE - 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In this individual contributor role, you will work at the cutting edge of power systems and machine learning, developing and deploying innovative AI solutions to optimize the operation of electrical power grids.</p>\n<p>Your work will be critical to delivering a real-world validation of our approach, with a primary focus on core software engineering tasks to:</p>\n<p>Enable rapid, trustworthy experimentation. Maintain rigorous benchmarking and testing. Manage scale for both data and model size. Ensure and maintain high data quality for both real-world and synthetic data.</p>\n<p><strong>Key Responsibilities</strong></p>\n<ul>\n<li>Design, implement, and maintain robust and reliable systems and workflows for generating large-scale synthetic and real datasets of power grid optimization problems.</li>\n<li>Design and implement rigorous unit, integration, and system tests to ensure the reliability, accuracy, and maintained performance of our models and software, with a focus on data pipelines.</li>\n<li>Maintain and contribute to our machine learning codebase, ensuring efficient data structures and seamless integration with our power system models and optimization solvers.</li>\n<li>Ensure the codebase supports ongoing experimentation, while simultaneously increasing scalability, robustness, and reliability via improved integration testing and performance benchmarking.</li>\n<li>Work closely and collaboratively with a team of engineers, research scientists, and product managers to deliver real-world impact.</li>\n</ul>\n<p><strong>Minimum Qualifications</strong></p>\n<ul>\n<li>Bachelor&#39;s degree in Computer Science, Software Engineering, or equivalent practical experience.</li>\n<li>Excellent proficiency in C++, Python, or Jax.</li>\n<li>Demonstrated experience developing or utilizing solutions for robustness or quality assurance within software and/or ML systems.</li>\n<li>Experience processing, generating, and analyzing large-scale data, e.g. for ML applications.</li>\n<li>Proven ability to discuss technical ideas effectively and collaborate in interdisciplinary teams.</li>\n<li>Motivated by the prospect of real-world impact and focused on excellence in software development.</li>\n</ul>\n<p><strong>Preferred Qualifications</strong></p>\n<ul>\n<li>Experience with Google&#39;s technical stack and/or Google Cloud Platform (GCP).</li>\n<li>Familiarity with modern hardware accelerators (GPU / TPU).</li>\n<li>Experience with modern ML training frameworks, such as Jax.</li>\n<li>Experience in developing software in a translational research or production setting.</li>\n<li>Proficiency in Julia</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_1ee5ad51-8f0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7750738","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"contract","x-salary-range":null,"x-skills-required":["C++","Python","Jax","Robustness","Quality Assurance","Software Development","Machine Learning","Data Analysis"],"x-skills-preferred":["Google's technical stack","Google Cloud Platform (GCP)","Modern hardware accelerators (GPU / TPU)","Modern ML training frameworks (Jax)","Software development in a translational research or production setting","Proficiency in Julia"],"datePosted":"2026-04-18T15:40:16.781Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"CONTRACTOR","occupationalCategory":"Engineering","industry":"Technology","skills":"C++, Python, Jax, Robustness, Quality Assurance, Software Development, Machine Learning, Data Analysis, Google's technical stack, Google Cloud Platform (GCP), Modern hardware accelerators (GPU / TPU), Modern ML training frameworks (Jax), Software development in a translational research or production setting, Proficiency in Julia"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_28107212-128"},"title":"Performance Engineer, GPU","description":"<p>As a GPU Performance Engineer at Anthropic, you will be responsible for architecting and implementing the foundational systems that power Claude and push the frontiers of what&#39;s possible with large language models. 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Your work will span the entire stack,from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.</p>\n<p>Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Architect and implement foundational systems that power Claude</li>\n<li>Maximize GPU utilization and performance at unprecedented scale</li>\n<li>Develop cutting-edge optimizations that directly enable new model capabilities</li>\n<li>Dramatically improve inference efficiency</li>\n<li>Implement state-of-the-art techniques from custom kernel development to distributed system architectures</li>\n<li>Work at the intersection of hardware and software</li>\n<li>Span the entire stack,from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Deep experience with GPU programming and optimization at scale</li>\n<li>Impact-driven, passionate about delivering measurable performance breakthroughs</li>\n<li>Ability to navigate complex systems from hardware interfaces to high-level ML frameworks</li>\n<li>Enjoy collaborative problem-solving and pair programming</li>\n<li>Want to work on state-of-the-art language models with real-world impact</li>\n<li>Care about the societal impacts of your work</li>\n<li>Thrive in ambiguous environments where you define the path forward</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Experience with GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization</li>\n<li>ML Compilers &amp; 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As a Research Scientist, you will apply and develop data and algorithmic cutting-edge solutions to advance our latest user-facing models. Your work will focus on advancing the safety and fairness behavior of state-of-the-art AI models, driving the development of foundational technology adopted by numerous product areas, including Gemini App, Cloud API, and Search.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Post-training/instruction tuning state-of-the-art LLMs, focusing on text-to-text, image/video/audio-to-text modalities and agentic capabilities</li>\n<li>Exploring data, reasoning, and algorithmic solutions to ensure Gemini Models are safe, maximally helpful, and work for everyone</li>\n<li>Improve Gemini&#39;s adversarial robustness, with a focus on high-stakes abuse risks</li>\n<li>Design and maintain high-quality evaluation protocols to assess model behavior gaps and headroom related to safety and fairness</li>\n<li>Develop and execute experimental plans to address known gaps, or construct entirely new capabilities</li>\n<li>Drive innovation and enhance understanding of Supervised Fine Tuning and Reinforcement Learning fine-tuning at scale</li>\n</ul>\n<p>To succeed as a Research Scientist in the Gemini Safety team, we look for the following skills and experience:</p>\n<ul>\n<li>PhD in Computer Science, a related field, or equivalent practical experience</li>\n<li>Significant LLM post-training experience</li>\n<li>Experience in Reward modeling and Reinforcement Learning for LLMs Instruction tuning</li>\n<li>Experience with Long-range Reinforcement learning</li>\n<li>Experience in areas such as Safety, Fairness, and Alignment</li>\n<li>Track record of publications at NeurIPS, ICLR, ICML</li>\n<li>Experience taking research from concept to product</li>\n<li>Experience with collaborating or leading an applied research project</li>\n<li>Strong experimental taste: Good judgment regarding baselines, ablations, and what is worth testing</li>\n<li>Experience with JAX</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_d2f5b1e5-545","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7731944","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PhD in Computer Science","LLM post-training experience","Reward modeling and Reinforcement Learning for LLMs Instruction tuning","Long-range Reinforcement learning","Safety, Fairness, and Alignment","NeurIPS, ICLR, ICML publications","Research from concept to product","Collaborating or leading an applied research project","JAX"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:40:08.109Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Zurich, Switzerland"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD in Computer Science, LLM post-training experience, Reward modeling and Reinforcement Learning for LLMs Instruction tuning, Long-range Reinforcement learning, Safety, Fairness, and Alignment, NeurIPS, ICLR, ICML publications, Research from concept to product, Collaborating or leading an applied research project, JAX"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_aa7ebb20-cd1"},"title":"Research Engineer, Post-Training for Code Security Analysis","description":"<p>JOB DESCRIPTION:</p>\n<p>About Us</p>\n<p>Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.</p>\n<p><strong>The Role</strong></p>\n<p>In this role, you&#39;ll work with a team of elite researchers and engineers to design and implement post-training strategies that enhance Gemini’s capabilities in code security analysis. You will bring contributions to our ML innovation, post-training refinement (SFT/RLHF), advanced evaluation, and data generation to ensure our models can reliably perform safe and powerful code security analysis.</p>\n<p><strong>Key responsibilities:</strong></p>\n<ul>\n<li>Design and Implement advanced post-training algorithms (SFT, RLHF, RLAIF) to optimize Gemini for code security tasks and secure coding practices.</li>\n</ul>\n<ul>\n<li>Diagnose and interpret training outcomes (regressions in coding ability, gains in security reasoning), and propose solutions to improve model capabilities.</li>\n</ul>\n<ul>\n<li>Actively monitor and evolve the system&#39;s performance through metric design.</li>\n</ul>\n<ul>\n<li>Develop reliable automated evaluation pipelines for code security that are strongly correlated with human security expert judgment.</li>\n</ul>\n<ul>\n<li>Construct complex benchmarks to probe the limits of the model’s ability to reason about control flow, memory safety, and software weakness.</li>\n</ul>\n<p><strong>About You</strong></p>\n<p>We are seeking individuals who excel in fast-pacing environments and are eager to contribute to the advancement of AI. We highly value the ability to invent novel solutions to complex problems, embracing a can-do and fail-fast mindset. We are looking for someone who genuinely believes in the future of AI and is committed to devoting their energy in this field.</p>\n<p>In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:</p>\n<ul>\n<li>BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry</li>\n</ul>\n<ul>\n<li>Deep understanding of statistics is strongly preferred</li>\n</ul>\n<ul>\n<li>Experiences in fine-tuning and adaptation of LLMs (e.g. advanced prompting, supervised fine-tuning, RLHF)</li>\n</ul>\n<ul>\n<li>Strong knowledge of systems design and data structures</li>\n</ul>\n<ul>\n<li>Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks</li>\n</ul>\n<ul>\n<li>Recent experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models</li>\n</ul>\n<ul>\n<li>A passion for Artificial Intelligence.</li>\n</ul>\n<ul>\n<li>Excellent communication skills and proven interpersonal skills, with a track record of effective collaboration with cross-functional teams</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_aa7ebb20-cd1","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7397549","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry","Deep understanding of statistics","Experiences in fine-tuning and adaptation of LLMs","Strong knowledge of systems design and data structures","Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks"],"x-skills-preferred":["A passion for Artificial Intelligence","Excellent communication skills and proven interpersonal skills, with a track record of effective collaboration with cross-functional teams"],"datePosted":"2026-04-18T15:39:01.360Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"BSc, MSc or PhD/DPhil degree in computer science, stats, machine learning or similar experience working in industry, Deep understanding of statistics, Experiences in fine-tuning and adaptation of LLMs, Strong knowledge of systems design and data structures, Proven experience with TensorFlow, JAX, PyTorch, or similar leading deep learning frameworks, A passion for Artificial Intelligence, Excellent communication skills and proven interpersonal skills, with a track record of effective collaboration with cross-functional teams"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_e121da52-304"},"title":"Research Engineer, Human Understanding","description":"<p>We are seeking a highly motivated Research Engineer with a strong background in multi-modal modelling for humans and a focus on speech &amp; audio/visual to join the effort within Google DeepMind&#39;s Frontier AI unit.</p>\n<p>This role is pivotal in developing foundational multimodal AI capabilities to understand, generate, and protect human likeness. As a key contributor, you will design and implement cutting-edge models and frameworks, pushing the boundaries of AI to enable foundational capabilities for human-centric understanding and generation.</p>\n<p>This is a unique opportunity to contribute to impactful research and advance Google DeepMind&#39;s mission towards Artificial General Intelligence (AGI).</p>\n<p><strong>Key Responsibilities</strong></p>\n<ul>\n<li>Advance multimodal human representations &amp; understanding: Research and implement novel models and other multimodal techniques for a more holistic understanding of humans across visual, audio, and textual data.</li>\n<li>Conduct applied research: Conduct experimental research cycles from hypothesis to deployment.</li>\n<li>Drive technical projects: Take ownership of substantial technical projects within the effort, from ideation and design to implementation and evaluation, often involving cross-functional collaboration.</li>\n<li>Contribute to Infrastructure: Inform and contribute to the development of scalable and efficient research infrastructure for multimodal human understanding models and datasets.</li>\n<li>Design and execute strategies for tuning and adapting VLMs and other foundation models for specific tasks</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>PhD degree in Computer Science, Machine Learning, or a related technical field with 3+ years of relevant experience.</li>\n<li>Experience in developing machine learning models, such as audio &amp; speech-visual models.</li>\n<li>Experience in working with and tuning large-scale vision language models.</li>\n<li>Strong programming skills in Python and experience with at least one major deep learning framework (e.g., JAX)</li>\n<li>Experience conducting independent research and development, including experimental design, implementation, and analysis.</li>\n</ul>\n<p><strong>Salary</strong></p>\n<p>The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + 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_e121da52-304","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7669433","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$174,000 USD - $252,000 USD","x-skills-required":["Python","JAX","Machine Learning","Deep Learning","Vision Language Models","Audio & Speech-Visual Models"],"x-skills-preferred":["Generative AI","Reinforcement Learning","Alignment Methods","Multimodal Learning","Privacy-Preserving Machine Learning"],"datePosted":"2026-04-18T15:38:13.994Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Los Angeles, California, US; 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Advance understanding and generation across modalities,image, video, audio, and text,spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.</p>\n<p>Collaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. 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You will build at the intersection of research, engineering and product, bridging disciplines by drawing on huge, one-of-a-kind proprietary datasets of music, metadata and user interactions/feedback.</p>\n<p>Design &amp; own evaluation/optimization frameworks for frontier music models. Dive deep under the hood of our music generation systems, applying computational &amp; human resources to understand model capabilities and identify areas for growth. Build optimization loops and apply your findings to our pretraining, post-training and inference systems as applicable.</p>\n<p>Drive product &amp; research roadmap. Own our data roadmap end-to-end, formulating research questions, exploring/linking/expanding data sources and conducting experiments at your discretion. Your work will span data mining, machine learning, causal inference, survey design and more, and your results will be critical for decision-making in product development, research investment and overall business direction.</p>\n<p>Build stable infrastructure. Your work will reach far beyond the jupyter kernel, manifesting in robust integrations with our research &amp; product tech stacks, potentially in performance-critical paths. You&#39;ll also build large-scale standalone data processing systems, allocating resources as needed to manage the data ecosystem.</p>\n<p>Champion scientific rigor. As our first quantitative researcher, you&#39;ll cultivate a culture of scientific rigor across the company and deepen common understanding of models, users and data. You&#39;ll proactively identify opportunities, define metrics, share results, and build a rigorous foundation upon which to understand our highly subjective domain.</p>\n<p>We&#39;re looking for someone with deep quantitative expertise, preferably a Ph.D. in statistics, mathematics, physics, or another quantitative discipline, or 5+ years&#39; industry experience as a quantitative analyst / data scientist. Autonomy &amp; ownership are key, as you&#39;ll thrive in greenfield research domains, undefined product categories and small, flat teams. Engineering chops are also important, as you&#39;ll need to translate your ideas into clear, production-ready code and collaborate in an active research codebase.</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_980a6242-1cf","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Udio","sameAs":"https://udio.com","logo":"https://logos.yubhub.co/udio.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/udio/jobs/5081608008","x-work-arrangement":"remote","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$250k - $350k","x-skills-required":["Ph.D. in statistics, mathematics, physics, or another quantitative discipline","5+ years' industry experience as a quantitative analyst / data scientist","Deep learning frameworks","JAX","GCP","Apache Beam/DataFlow","Kubernetes","TensorFlow Data / TFRecord"],"x-skills-preferred":["Obsession with music & the science of sound","Experience in DSP, MIR, music production / composition / performance","Big record collection"],"datePosted":"2026-04-17T13:06:29.547Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York City (Remote possible for exceptional candidates)"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Ph.D. in statistics, mathematics, physics, or another quantitative discipline, 5+ years' industry experience as a quantitative analyst / data scientist, Deep learning frameworks, JAX, GCP, Apache Beam/DataFlow, Kubernetes, TensorFlow Data / TFRecord, Obsession with music & the science of sound, Experience in DSP, MIR, music production / composition / performance, Big record collection","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":350000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_2bf2ef11-7d6"},"title":"Senior Backend Engineer, Data Modeling and Ingestion Platform","description":"<p>We are looking for a Senior Backend Engineer to lead the unification of large, highly rich, and heterogeneous datasets sourced from a wide range of external providers. These datasets are used to power our generative audio models.</p>\n<p>Your work will create the foundational dataset that powers our research by building robust, scalable systems for linking, deduplicating, reconciling, and enriching data at massive scale. This role centres on high-impact bulk ingestion and advanced data linkage. You will design the logic, algorithms, and strategies that transform many independent datasets into a unified, high-quality canonical asset used throughout the company.</p>\n<p>You will collaborate closely with ML researchers and product teams, working with tools such as BigQuery, Dataflow/Beam, TFRecords, and,where beneficial,distributed systems frameworks like Ray. Familiarity with ML workflows using JAX or multihost training is a plus, as the datasets you produce will directly support that ecosystem.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build high-throughput bulk ingestion workflows to integrate datasets from multiple external providers.</li>\n<li>Design and implement scalable entity-resolution solutions, including record linking, deduplication, clustering, and conflict arbitration.</li>\n<li>Create and refine matching logic, decision rules, and similarity functions to align datasets with high accuracy and strong coverage.</li>\n<li>Define and track data quality indicators, such as overlap metrics, match precision/recall, duplicate rates, and completeness.</li>\n<li>Prepare training-ready datasets in formats such as TFRecords, and structure data to meet ML research requirements.</li>\n<li>Develop processing components using Dataflow (Beam) and manage large analytical workloads in BigQuery.</li>\n<li>Leverage frameworks like Ray to accelerate large-scale experiments, feature extraction, and research-oriented data preparation.</li>\n<li>Collaborate with ML researchers to anticipate downstream requirements and evolve linkage strategies as new sources and use cases emerge.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Experience working with large, heterogeneous datasets from multiple providers or domains.</li>\n<li>Strong background in entity resolution, deduplication, data unification, or related large-scale data integration techniques.</li>\n<li>Proficiency in Python, with an emphasis on efficient, scalable data processing.</li>\n<li>Experience with BigQuery, Google Dataflow/Apache Beam, or similar batch-processing frameworks.</li>\n<li>Familiarity with data validation, normalization, reconciliation, and building consistent views across diverse data sources.</li>\n<li>Ability to craft well-structured matching and decision strategies that balance accuracy, completeness, and computational efficiency.</li>\n<li>Comfortable iterating quickly on pragmatic solutions, balancing correctness with time-to-delivery.</li>\n<li>Clear communication skills and the ability to collaborate closely with ML and research teams.</li>\n</ul>\n<p>Nice to Have:</p>\n<ul>\n<li>Knowledge of architecting Google Cloud Platform systems at scale.</li>\n<li>Experience with distributed compute frameworks such as Ray, Spark, or Flink.</li>\n<li>Understanding of JAX-based ML pipelines, multihost training setups, or large-scale data preparation for accelerator-backed workflows.</li>\n<li>Familiarity with TFRecords or other high-volume training data formats.</li>\n<li>Exposure to ranking, clustering, or statistical similarity modeling.</li>\n<li>Experience with Go, NextJS, and/or React Native to contribute to full-stack development.</li>\n</ul>\n<p>Why Join Us:</p>\n<ul>\n<li>You will design the core dataset that underpins our research, product development, and generative audio models.</li>\n<li>You&#39;ll work on large-scale data challenges that require creativity, algorithmic thinking, and engineering excellence.</li>\n<li>You&#39;ll join a small, fast-moving team where your decisions shape the direction of our data and research capabilities.</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Highly competitive salary and equity.</li>\n<li>Quarterly productivity budget.</li>\n<li>Flexible time off.</li>\n<li>Fantastic office location in Manhattan.</li>\n<li>Productivity package, including ChatGPT Plus, Claude Code, and Copilot.</li>\n<li>Top-notch private health, dental, and vision insurance for you and your dependents.</li>\n<li>401(k) plan options with employer matching.</li>\n<li>Concierge medical/primary care through One Medical and Rightway.</li>\n<li>Mental health support from Spring Health.</li>\n<li>Personalized life insurance, travel assistance, and many other perks.</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_2bf2ef11-7d6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Udio","sameAs":"https://udio.com","logo":"https://logos.yubhub.co/udio.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/udio/jobs/4988140008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$180,000 - 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Our technology is designed to integrate seamlessly into daily working life.</p>\n<p>We are a global company with teams distributed between France, USA, UK, Germany, and Singapore. We offer a comprehensive AI platform that meets enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work.</p>\n<p>Role Summary</p>\n<p>Mistral AI is seeking Applied Scientists Interns and Research Engineers Interns to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas, you will apply these models across a diverse set of use cases and domains.</p>\n<p>Responsibilities</p>\n<p>• Run pre-training, post-training, and deploy state-of-the-art models on clusters with thousands of GPUs.\n• Generate and curate data for pre-training and post-training, working on evaluations and making sure the model&#39;s performance beats expectations.\n• Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation, and deployment.\n• Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines.\n• Manage research projects and communications with client research teams.</p>\n<p>About You</p>\n<p>• You are fluent in English, and have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences.\n• You&#39;re an expert with PyTorch or JAX.\n• You&#39;re not afraid of contributing to a big codebase and can find yourself around independently with little guidance.\n• You write clean, readable, high-performance, fault-tolerant Python code.\n• You don&#39;t need roadmaps: you just do. You don&#39;t need a manager: you just ship.\n• Low-ego, collaborative, and eager to learn.\n• You have a track record of success through personal projects, professional projects, or in academia.</p>\n<p>Benefits</p>\n<p>• Competitive salary\n• Food: Daily lunch vouchers\n• Sport: Monthly contribution to a Gympass subscription\n• Transportation: Monthly contribution to a mobility pass</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_9e926934-312","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai","logo":"https://logos.yubhub.co/mistral.ai.png"},"x-apply-url":"https://jobs.lever.co/mistral/426ef8c0-eb26-4004-a690-f33c62b445a7","x-work-arrangement":"onsite","x-experience-level":"entry","x-job-type":"internship","x-salary-range":null,"x-skills-required":["PyTorch","JAX","Python","GPU","data generation","model training","evaluation","deployment"],"x-skills-preferred":["agents","multi-modality","robotics","diffusion models","time-series analysis"],"datePosted":"2026-04-17T12:47:54.108Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Paris"}},"employmentType":"INTERN","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, Python, GPU, data generation, model training, evaluation, deployment, agents, multi-modality, robotics, diffusion models, time-series analysis"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_81c1220e-c71"},"title":"Applied Scientist / Research Engineer","description":"<p>About the Job</p>\n<p>Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. 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They believe in the power of AI to simplify tasks, save time, and enhance learning and creativity.</p>\n<p>Role Summary</p>\n<p>The Research Engineering team at Mistral AI spans Platform (shared infra &amp; clean code) and Embedded (inside research squads). Engineers can move along the research↔production spectrum as needs or interests evolve. As a Research Engineer – ML track, you’ll build and optimise the large-scale learning systems that power their open-weight models.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.</li>\n<li>Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.</li>\n<li>Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs).</li>\n<li>Design, implement and benchmark ML algorithms; write clear, efficient code in Python.</li>\n<li>Deliver prototypes that become production-grade components for Le Chat and their enterprise API.</li>\n</ul>\n<p>Requirements</p>\n<ul>\n<li>Master’s or PhD in Computer Science (or equivalent proven track record).</li>\n<li>4 + years working on large-scale ML codebases.</li>\n<li>Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).</li>\n<li>Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.</li>\n<li>Strong software-design instincts: testing, code review, CI/CD.</li>\n<li>Self-starter, low-ego, collaborative.</li>\n</ul>\n<p>What we offer</p>\n<ul>\n<li>Competitive salary and equity.</li>\n<li>Healthcare: Medical/Dental/Vision covered for you and your family.</li>\n<li>Pension: 401K (6% matching)</li>\n<li>PTO: 18 days</li>\n<li>Transportation: Reimburse office parking charges, or $120/month for public transport</li>\n<li>Sport: $120/month reimbursement for gym membership</li>\n<li>Meal stipend: $400 monthly allowance for meals (solution might evolve as they grow bigger)</li>\n<li>Visa sponsorship</li>\n<li>Coaching: they offer BetterUp coaching on a voluntary basis</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_d2256e99-10a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai/careers","logo":"https://logos.yubhub.co/mistral.ai.png"},"x-apply-url":"https://jobs.lever.co/mistral/bada0014-0f32-4370-b55f-81c5595c7339","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PyTorch","JAX","TensorFlow","Distributed training","Deep learning","NLP","LLMs","CUDA","Data pipeline"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:47:41.659Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, TensorFlow, Distributed training, Deep learning, NLP, LLMs, CUDA, Data pipeline"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c569b4c8-3f2"},"title":"Applied Scientist / Research Engineer - EMEA","description":"<p>About Mistral AI</p>\n<p>Mistral AI is a pioneering company shaping the future of AI. We believe in the power of AI to simplify tasks, save time, and enhance learning and creativity.</p>\n<p>About The Job</p>\n<p>We are seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains.</p>\n<p>Responsibilities</p>\n<p>• Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. You don’t panic when you see OOM errors or when NCCL feels like not wanting to talk.\n• Generate and curate data for pre-training and post-training, working on evaluations and making sure the model’s performance beats expectations.\n• Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment.\n• Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines.\n• Manage research projects and communications with client research teams.</p>\n<p>About You</p>\n<p>• You are fluent in English, and have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences.\n• You’re an expert with PyTorch or JAX.\n• You’re not afraid of contributing to a big codebase and can find yourself around independently with little guidance.\n• You write clean, readable, high-performance, fault-tolerant Python code.\n• You don’t need roadmaps: you just do. You don’t need a manager: you just ship.\n• Low-ego, collaborative and eager to learn.\n• You have a track record of success through personal projects, professional projects or in academia.</p>\n<p>Benefits</p>\n<p>We have local offices in Paris, London, Marseille, Singapore and Palo Alto. France:\n• Competitive cash salary and equity\n• Food: Daily lunch vouchers\n• Sport: Monthly contribution to a Gympass subscription\n• Transportation: Monthly contribution to a mobility pass\n• Health: Full health insurance for you and your family\n• Parental: Generous parental leave policy\n• Visa sponsorship</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_c569b4c8-3f2","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai/careers","logo":"https://logos.yubhub.co/mistral.ai.png"},"x-apply-url":"https://jobs.lever.co/mistral/b7ae8fc4-5779-4ad2-8f5b-632b4d9498cf","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PyTorch","JAX","Python","GPU","OOM errors","NCCL"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:47:06.801Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Paris"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, Python, GPU, OOM errors, NCCL"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_50cacac8-b47"},"title":"Research Engineer, Machine Learning","description":"<p><strong>About the Role</strong></p>\n<p>We are seeking a Research Engineer to join our Machine Learning team. As a Research Engineer, you will work on building and optimizing large-scale learning systems that power our open-weight models.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.</li>\n<li>Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.</li>\n<li>Conduct experiments on the latest deep-learning techniques.</li>\n<li>Design, implement and benchmark ML algorithms; write clear, efficient code in Python.</li>\n<li>Deliver prototypes that become production-grade components for Le Chat and our enterprise API.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Master&#39;s or PhD in Computer Science (or equivalent proven track record).</li>\n<li>4 + years working on large-scale ML codebases.</li>\n<li>Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).</li>\n<li>Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.</li>\n<li>Strong software-design instincts: testing, code review, CI/CD.</li>\n<li>Self-starter, low-ego, collaborative.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive cash salary and equity.</li>\n<li>Food: Daily lunch vouchers.</li>\n<li>Sport: Monthly contribution to a Gympass subscription.</li>\n<li>Transportation: Monthly contribution to a mobility pass.</li>\n<li>Health: Full health insurance for you and your family.</li>\n<li>Parental: Generous parental leave policy.</li>\n</ul>\n<p>Note: Benefits may vary depending on location.</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_50cacac8-b47","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai/careers","logo":"https://logos.yubhub.co/mistral.ai.png"},"x-apply-url":"https://jobs.lever.co/mistral/07447e1d-7900-46d4-b61b-186f2f76847f","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PyTorch","JAX","TensorFlow","DeepSpeed","FSDP","SLURM","K8s","Python","CUDA","data-pipeline"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:47:05.094Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Paris"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, TensorFlow, DeepSpeed, FSDP, SLURM, K8s, Python, CUDA, data-pipeline"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_eb286b66-47b"},"title":"AI Scientist","description":"<p>About this role</p>\n<p>We&#39;re looking for an AI Scientist to join our research team in Zurich. As an AI Scientist, you will research and develop novel methods to push the frontier of large language models. You will work across use cases (e.g., reasoning, code, agents) and modalities (e.g., text, image, and speech). You will build tooling and infrastructure to allow training, evaluation, and analysis of AI models at scale. You will also work cross-functionally with other scientists, engineers, and product teams to ship AI systems that have a real-world impact.</p>\n<p>About you</p>\n<ul>\n<li>You are a highly proficient software engineer in at least one programming language (Python or other, e.g., Rust, Go, Java).</li>\n<li>You have hands-on experience with AI frameworks (e.g., PyTorch, JAX) or distributed systems (e.g., Ray, Kubernetes).</li>\n<li>You have high engineering competence. 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We develop models for the enterprise and for consumers, focusing on delivering systems which can really change the way in which businesses operate and which can integrate into our daily lives.</p>\n<p>What will you do?</p>\n<ul>\n<li>Research and develop novel methods to push the frontier of large language models</li>\n<li>Work across use cases (e.g reasoning, code, agents) and modalities (e.g text, image and speech)</li>\n<li>Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale</li>\n<li>Work cross-functionally with other scientists, engineers and product teams to ship AI systems which have a real-world impact</li>\n</ul>\n<p>About you</p>\n<ul>\n<li>You are a highly proficient software engineer in at least one programming language (Python or other, e.g. Rust, Go, Java)</li>\n<li>You have hands-on experience with AI frameworks (e.g. PyTorch, JAX) or distributed systems (e.g. Ray, Kubernetes)</li>\n<li>You have high engineering competence. 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All while releasing frontier models open-source, for everyone to try and benefit.</p>\n<p>What will you do?</p>\n<ul>\n<li>Research and develop novel methods to push the frontier of large language models</li>\n<li>Work across use cases (e.g reasoning, code, agents) and modalities (e.g text, image and speech)</li>\n<li>Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale</li>\n<li>Work cross-functionally with other scientists, engineers and product teams to ship AI systems which have a real-world impact</li>\n</ul>\n<p>About you</p>\n<ul>\n<li>An expert in speech input/output methodologies (specific to audio)</li>\n<li>Highly proficient software engineer in at least one programming language (Python or other, e.g. Rust, Go, Java)</li>\n<li>Hands-on experience with AI frameworks (e.g. PyTorch, JAX) or distributed systems (e.g. Ray, Kubernetes)</li>\n<li>High engineering competence. 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The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.</p>\n<p>They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organisation dedicated to changing the entire landscape of cancer.</p>\n<p>The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Independently pursue cutting-edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.)</li>\n<li>Build new models or fine-tune existing models to identify biological changes resulting from disease</li>\n<li>Build models that achieve high accuracy and that generalise robustly to new data</li>\n<li>Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms</li>\n<li>Work closely with ML Engineering partners to ensure that Freenome&#39;s computational infrastructure supports optimal model training and iteration</li>\n<li>Take a mindful, transparent, and humane approach to your work</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics</li>\n<li>6+ years of post-doc or post-PhD industry experience achieving impactful results using relevant modelling techniques</li>\n<li>Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modelling</li>\n<li>Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation</li>\n<li>Practical and theoretical understanding of DL models like large language models or other foundation models</li>\n<li>Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning</li>\n<li>Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data</li>\n<li>Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.</li>\n<li>Proficiency in one or more ML frameworks such as; PyTorch, TensorFlow and JAX; and ML platforms like Hugging Face</li>\n<li>Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights &amp; Biases</li>\n<li>Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations</li>\n<li>Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists</li>\n<li>A passion for innovation and demonstrated initiative in tackling new areas of research</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Deep domain-specific experience in computational biology, genomics, proteomics or a related field</li>\n<li>Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models</li>\n<li>Experience in NGS data analysis and bioinformatic pipelines</li>\n<li>Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS</li>\n<li>Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems</li>\n</ul>\n<p>Benefits and additional information:</p>\n<ul>\n<li>The US target range of our base salary for new hires is $199,675.00 - $283,500.00. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered. Please note that individual total compensation for this position will be determined at the Company&#39;s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education.</li>\n<li>Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.</li>\n<li>Applicants have rights under Federal Employment Laws.</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_c77545f4-627","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Freenome","sameAs":"https://freenome.com/","logo":"https://logos.yubhub.co/freenome.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/freenome/jobs/8215797002","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$199,675.00 - $283,500.00","x-skills-required":["Artificial Intelligence","Machine Learning","Deep Learning","Computational Biology","Genomics","Immunology","Python","R","Java","C","C++","PyTorch","TensorFlow","JAX","Hugging Face","TensorBoard","MLflow","Weights & Biases"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:35:13.294Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Brisbane, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Healthcare","skills":"Artificial Intelligence, Machine Learning, Deep Learning, Computational Biology, Genomics, Immunology, Python, R, Java, C, C++, PyTorch, TensorFlow, JAX, Hugging Face, TensorBoard, MLflow, Weights & Biases","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":199675,"maxValue":283500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_faec8dc3-4d3"},"title":"Senior Machine Learning Scientist","description":"<p>We are seeking a Senior Machine Learning Scientist to help grow the Machine Learning Science team. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods. They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organisation dedicated to changing the entire landscape of cancer.</p>\n<p>The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Independently pursuing cutting-edge research in AI applied to biological problems</li>\n<li>Building new models or fine-tuning existing models to identify biological changes resulting from disease</li>\n<li>Building models that achieve high accuracy and that generalise robustly to new data</li>\n<li>Applying contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms</li>\n<li>Working closely with ML Engineering partners to ensure that Freenome&#39;s computational infrastructure supports optimal model training and iteration</li>\n<li>Taking a mindful, transparent, and humane approach to your work</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics</li>\n<li>3+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modelling techniques</li>\n<li>Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modelling</li>\n<li>Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, neural networks</li>\n<li>Practical and theoretical understanding of DL models like large language models or other foundation models</li>\n<li>Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning</li>\n<li>Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data</li>\n<li>Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.</li>\n<li>Proficiency in one or more ML frameworks such as; 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The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment.</p>\n<p>The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimising hardware utilisation for efficient training, and performing model optimisations.</p>\n<p>As part of an interdisciplinary R&amp;D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Implementing and refining DL pipelines on distributed computing platforms to enhance the speed and efficiency of DL operations, including model training, data handling, model management, and inference.</li>\n<li>Collaborating closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines created are perfectly aligned with scientific goals and operational needs.</li>\n<li>Continuously monitoring, evaluating, and optimising DL model training pipelines for performance and scalability.</li>\n<li>Staying up to date with the latest advancements in AI, ML, and related technologies, and quickly learning and adapting new tools and frameworks, if necessary.</li>\n<li>Developing and maintaining robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results.</li>\n<li>Driving performance improvements across our stack through profiling, optimisation, and benchmarking. Implementing efficient caching solutions and debugging distributed systems to accelerate both training and evaluation pipelines.</li>\n<li>Acting as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement.</li>\n</ul>\n<p>Must-haves include:</p>\n<ul>\n<li>MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.</li>\n<li>5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines.</li>\n<li>Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc.</li>\n<li>Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn.</li>\n<li>In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.</li>\n<li>Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment.</li>\n<li>Understanding of containerisation technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions.</li>\n<li>Proven track record of developing and optimising workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume.</li>\n<li>Experience managing large datasets, including data storage (such as HDFS or Parquet on S3), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark).</li>\n<li>Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows.</li>\n<li>Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team.</li>\n<li>Excellent ability to work effectively with cross-functional teams and communicate across disciplines.</li>\n</ul>\n<p>Nice-to-haves include:</p>\n<ul>\n<li>Experience working with large-scale genomics or biological datasets.</li>\n<li>Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data.</li>\n<li>Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA).</li>\n<li>Experience with infrastructure-as-code and configuration management.</li>\n<li>Experience cultivating MLOps and ML infrastructure best practices, especially around reliability, provisioning and monitoring.</li>\n<li>Strong track record of contributions to relevant DL projects, e.g. on github.</li>\n</ul>\n<p>The US target range of our base salary for new hires is $161,925 - $227,325. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.</p>\n<p>Freenome is proud to be an equal-opportunity employer, and we value diversity. 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Join us to be part of a pioneering company shaping the future of AI.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Research and develop novel methods to push the frontier of large language models</li>\n<li>Work across use cases (e.g reasoning, code, agents) and modalities (e.g text, image and speech)</li>\n<li>Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale</li>\n<li>Work cross-functionally with other scientists, engineers and product teams to ship AI systems which have a real-world impact</li>\n</ul>\n<p><strong>About You:</strong></p>\n<ul>\n<li>You are a highly proficient software engineer in at least one programming language (Python or other, e.g. Rust, Go, Java)</li>\n<li>You have hands-on experience with AI frameworks (e.g. PyTorch, JAX) or distributed systems (e.g. Ray, Kubernetes)</li>\n<li>You have high engineering competence. This means being able to design complex software and make it usable in production</li>\n<li>You are a self-starter, autonomous and a team player</li>\n</ul>\n<p><strong>Nice to Have:</strong></p>\n<ul>\n<li>You have hands-on experience with training large transformer models in a distributed fashion</li>\n<li>You are able to navigate the full MLOps stack, for instance, fine-tuning, evaluation and deployment</li>\n<li>You have a strong publication record in a relevant scientific domain</li>\n<li>Audio/Speech experience - audio input/out, NLP</li>\n</ul>\n<p><strong>What We Offer:</strong></p>\n<ul>\n<li>Competitive salary and bonus structure</li>\n<li>Generous Equity</li>\n<li>Health: Competitive Healthcare program (Medical Provider: Blueshield of California 100% coverage for employee, 75% for dependents)</li>\n<li>Pension: 401K (6% matching)</li>\n<li>PTO: 18 days</li>\n<li>Transportation: Reimburse office parking charges, or $120/month for public transport</li>\n<li>Coaching: we offer Betterup coaching on a voluntary basis</li>\n<li>Sport: $120/month reimbursement for gym membership</li>\n<li>Meal stipend: $400 monthly allowance for meals (solution might evolve as we grow bigger)</li>\n<li>Visa sponsorship</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_66a96d8c-777","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral","sameAs":"https://www.mistral.ai/"},"x-apply-url":"https://jobs.lever.co/mistral/7b20d2c8-d5a7-4efd-a13e-05d920ec5985","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","PyTorch","JAX","Rust","Go","Java","distributed systems","Ray","Kubernetes","large language models","transformer models"],"x-skills-preferred":["audio input/out","NLP","MLOps","fine-tuning","evaluation","deployment"],"datePosted":"2026-03-10T11:30:26.219Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, JAX, Rust, Go, Java, distributed systems, Ray, Kubernetes, large language models, transformer models, audio input/out, NLP, MLOps, fine-tuning, evaluation, deployment"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_89406e8e-f38"},"title":"Machine Learning Engineer, Open-Source Software","description":"<p>You will be in charge of open-sourcing state-of-the-art models, whilst maintaining and improving Mistral’s publicly available libraries. Your work is critical in helping turn research breakthroughs into tangible solutions and improve Mistral&#39;s open-source ecosystem.</p>\n<p>About the Open Source Software team\nOur OSS team is embedded in our Science team and works very closely with various engineering and marketing teams. All OSS team members can fluidly move on the production / research spectrum depending on where the needs are or where their interests lie</p>\n<p>Responsibilities\n• Releasing our models to open-source platforms and libraries, e.g., vLLM, GitHub, Hugging Face\n• Maintaining Mistral’s open-source libraries (mistral-common, mistral-finetune, mistral-inference)\n• Create and maintain tooling and services: both internal facing (internal research) and external facing (open-source libraries)\n• Implement and optimize open-source and internal libraries for performance and accuracy, ensuring production readiness and employing cutting-edge technology and innovative approaches\n• Collaborate with the open-source community (PyTorch, vLLM, Hugging Face)</p>\n<p>About you\n• Master’s degree in Computer Science, Machine Learning, Data Science, or a related field\n• Experience contributing to popular open-source libraries such as PyTorch, Tensorflow, JAX, vLLM, Transformers, Llama.cpp, ...\n• Passion for contributing to the open-source software ecosystem\n• Expert programming skills in Python, PyTorch, MLOps\n• Adaptable, proactive, and autonomous\n• Attention to detail and a drive to go the last mile to build almost perfect tools\n• Deep understanding of machine learning approaches, especially LLMs and algorithms\n• Low-ego, collaborative and have a real team player mindset</p>\n<p>Now, it would be ideal if you have:\n• Experience with training and fine-tuning large language models (e.g., distillation, supervised fine-tuning, policy optimization)\n• Experience working with Slurm\n• Worked with research teams before\n• Experience as a core-maintainer of a popular ML open-source library</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_89406e8e-f38","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai"},"x-apply-url":"https://jobs.lever.co/mistral/ef4c26fc-3fdb-4dd2-a64e-95264ee769dd","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","PyTorch","MLOps","Machine Learning","Large Language Models","Slurm","Open-source libraries"],"x-skills-preferred":["vLLM","GitHub","Hugging Face","PyTorch","Tensorflow","JAX","Transformers","Llama.cpp"],"datePosted":"2026-03-10T11:30:04.700Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Paris"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, MLOps, Machine Learning, Large Language Models, Slurm, Open-source libraries, vLLM, GitHub, Hugging Face, PyTorch, Tensorflow, JAX, Transformers, Llama.cpp"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6ef094ae-1e3"},"title":"AI Scientist - Paris/London - Onsite or Hybrid or Remote","description":"<p>At Mistral, we are on a mission to democratize AI, producing frontier intelligence for everyone, developed in the open, and built by engineers all over the world.</p>\n<p>We are hiring experts in the training of large language models and distributed systems. Join us to be part of a pioneering company shaping the future of AI.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Research and develop novel methods to push the frontier of large language models</li>\n<li>Work across use cases (e.g reasoning, code, agents) and modalities (e.g text, image and speech)</li>\n<li>Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale</li>\n<li>Work cross-functionally with other scientists, engineers and product teams to ship AI systems which have a real-world impact</li>\n</ul>\n<p>About you:</p>\n<ul>\n<li>You are a highly proficient software engineer in at least one programming language (Python or other, e.g. Rust, Go, Java)</li>\n<li>You have hands-on experience with AI frameworks (e.g. PyTorch, JAX) or distributed systems (e.g. Ray, Kubernetes)</li>\n<li>You have high engineering competence. 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We believe in the power of AI to simplify tasks, save time, and enhance learning and creativity.</p>\n<p>Role Summary</p>\n<p>Mistral AI is seeking Applied Scientists Interns and Research Engineers Interns to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs.</li>\n<li>Generate and curate data for pre-training and post-training, working on evaluations and making sure the model&#39;s performance beats expectations.</li>\n<li>Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment.</li>\n<li>Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines.</li>\n<li>Manage research projects and communications with client research teams.</li>\n</ul>\n<p>About You</p>\n<ul>\n<li>You are fluent in English, and have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences.</li>\n<li>You&#39;re an expert with PyTorch or JAX.</li>\n<li>You&#39;re not afraid of contributing to a big codebase and can find yourself around independently with little guidance.</li>\n<li>You write clean, readable, high-performance, fault-tolerant Python code.</li>\n<li>You don&#39;t need roadmaps: you just do. You don&#39;t need a manager: you just ship.</li>\n<li>Low-ego, collaborative and eager to learn.</li>\n<li>You have a track record of success through personal projects, professional projects or in academia.</li>\n</ul>\n<p>It Would Be Great If</p>\n<ul>\n<li>You are pursuing a PhD / master in a relevant field (e.g., Mathematics, Physics, Machine Learning), but if you’re an exceptional candidate from a different background, you should apply.</li>\n<li>We’d love to have you for at least 3 months, ideally 6 months.</li>\n<li>You can bring a variety of research experiences, such as working with agents, multi-modality, robotics, diffusion models, or time-series analysis.</li>\n<li>Have contributed to a large codebase used by many (open source or in the industry).</li>\n<li>Have a track record of publications in top academic journals or conferences.</li>\n<li>Love improving existing code by fixing typing issues, adding tests and improving CI pipelines.</li>\n</ul>\n<p>Benefits</p>\n<ul>\n<li>Competitive salary</li>\n<li>Food: Daily lunch vouchers</li>\n<li>Sport: Monthly contribution to a Gympass subscription</li>\n<li>Transportation: Monthly contribution to a mobility pass</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_fe73c8b8-4de","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai"},"x-apply-url":"https://jobs.lever.co/mistral/426ef8c0-eb26-4004-a690-f33c62b445a7","x-work-arrangement":"onsite","x-experience-level":"entry","x-job-type":"internship","x-salary-range":null,"x-skills-required":["PyTorch","JAX","Python","GPU","OOM errors","NCCL"],"x-skills-preferred":["agents","multi-modality","robotics","diffusion models","time-series analysis"],"datePosted":"2026-03-10T11:25:01.722Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Paris"}},"employmentType":"INTERN","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, Python, GPU, OOM errors, NCCL, agents, multi-modality, robotics, diffusion models, time-series analysis"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9bc334f7-f1c"},"title":"Applied Scientist / Research Engineer","description":"<p>About the Job</p>\n<p>Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects.</p>\n<p>You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions that turn the needle.</p>\n<p>Responsibilities</p>\n<p>• Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. You don’t panic when you see OOM errors or when NCCL feels like not wanting to talk.\n• Generate and curate data for pre-training and post-training, working on evaluations and making sure the model’s performance beats expectations.\n• Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment.\n• Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines.\n• Manage research projects and communications with client research teams.</p>\n<p>About You</p>\n<p>• You are fluent in English, and have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences.\n• You’re an expert with PyTorch or JAX.\n• You’re not afraid of contributing to a big codebase and can find yourself around independently with little guidance.\n• You write clean, readable, high-performance, fault-tolerant Python code.\n• You don’t need roadmaps: you just do. You don’t need a manager: you just ship.\n• Low-ego, collaborative and eager to learn.\n• You have a track record of success through personal projects, professional projects or in academia.</p>\n<p>It would be great if you</p>\n<p>• Hold a PhD / master in a relevant field (e.g., Mathematics, Physics, Machine Learning), but if you’re an exceptional candidate from a different background, you should apply.\n• Can bring a variety of research experience (agents, multi-modality, robotics, diffusion, time-series).\n• Have contributed to a large codebase used by many (open source or in the industry).\n• Have a track record of publications in top academic journals or conferences.\n• Love improving existing code by fixing typing issues, adding tests and improving CI pipelines.</p>\n<p>Benefits</p>\n<p>• Competitive cash salary and equity\n• Health Insurance\n• Sport: $90 for gym membership allowance\n• Food: $200 monthly allowance for meals (solution might evolve as we grow bigger)\n• Transportation: $120/month for public transport or Parking charges reimbursed</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_9bc334f7-f1c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai"},"x-apply-url":"https://jobs.lever.co/mistral/c41d9d9e-f0ea-4621-a4a9-3f10dfa9ae84","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PyTorch","JAX","Python","GPU","OOM","NCCL"],"x-skills-preferred":[],"datePosted":"2026-03-10T11:24:50.623Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Singapore"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, Python, GPU, OOM, NCCL"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_53060679-f73"},"title":"Applied Scientist / Research Engineer - EMEA","description":"<p>About Mistral AI</p>\n<p>At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.</p>\n<p>We are a global company with teams distributed between France, USA, UK, Germany, and Singapore. Our comprehensive AI platform meets enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work.</p>\n<p>About The Job</p>\n<p>Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas, you will apply these models across a diverse set of use cases and domains.</p>\n<p>Responsibilities</p>\n<p>• Run pre-training, post-training, and deploy state-of-the-art models on clusters with thousands of GPUs. You don&#39;t panic when you see OOM errors or when NCCL feels like not wanting to talk.\n• Generate and curate data for pre-training and post-training, working on evaluations and making sure the model&#39;s performance beats expectations.\n• Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation, and deployment.\n• Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines.\n• Manage research projects and communications with client research teams.</p>\n<p>About You</p>\n<p>• You are fluent in English, and have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences.\n• You&#39;re an expert with PyTorch or JAX.\n• You&#39;re not afraid of contributing to a big codebase and can find yourself around independently with little guidance.\n• You write clean, readable, high-performance, fault-tolerant Python code.\n• You don&#39;t need roadmaps: you just do. You don&#39;t need a manager: you just ship.\n• Low-ego, collaborative, and eager to learn.\n• You have a track record of success through personal projects, professional projects, or in academia.</p>\n<p>It would be great if you</p>\n<p>• Hold a PhD/master in a relevant field (e.g., Mathematics, Physics, Machine Learning), but if you&#39;re an exceptional candidate from a different background, you should apply.\n• Can bring a variety of research experience (agents, multi-modality, robotics, diffusion, time-series).\n• Have contributed to a large codebase used by many (open source or in the industry).\n• Have a track record of publications in top academic journals or conferences.\n• Love improving existing code by fixing typing issues, adding tests, and improving CI pipelines.</p>\n<p>Benefits</p>\n<p>We have local offices in Paris, London, Marseille, Singapore, and Palo Alto. France:\n• Competitive cash salary and equity\n• Food: Daily lunch vouchers\n• Sport: Monthly contribution to a Gympass subscription\n• Transportation: Monthly contribution to a mobility pass\n• Health: Full health insurance for you and your family\n• Parental: Generous parental leave policy\n• Visa sponsorship</p>\n<p>UK:\n• Competitive cash salary and equity\n• Insurance\n• Transportation: Reimburse office parking charges, or 90GBP/month for public transport\n• Sport: 90GBP/month reimbursement for gym membership\n• Meal voucher: £200 monthly allowance for meals\n• Pension plan: SmartPension (percentages are 5% Employee &amp; 3% Employer)</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_53060679-f73","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral AI","sameAs":"https://mistral.ai"},"x-apply-url":"https://jobs.lever.co/mistral/b7ae8fc4-5779-4ad2-8f5b-632b4d9498cf","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PyTorch","JAX","Python","Machine Learning","Research Engineering"],"x-skills-preferred":[],"datePosted":"2026-03-10T11:24:35.662Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Paris"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, Python, Machine Learning, Research Engineering"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1aefaf93-fe1"},"title":"AI Scientist","description":"<p>About Mistral AI</p>\n<p>At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.</p>\n<p>We are a global team with a presence in France, USA, UK, Germany, and Singapore. Our diverse workforce thrives in competitive environments and is committed to driving innovation.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Research and develop novel methods to push the frontier of large language models</li>\n<li>Work across use cases (e.g., reasoning, code, agents) and modalities (e.g., text, image, and speech)</li>\n<li>Build tooling and infrastructure to allow training, evaluation, and analysis of AI models at scale</li>\n<li>Work cross-functionally with other scientists, engineers, and product teams to ship AI systems that have a real-world impact</li>\n</ul>\n<p><strong>About You</strong></p>\n<ul>\n<li>You are a highly proficient software engineer in at least one programming language (Python or other, e.g., Rust, Go, Java)</li>\n<li>You have hands-on experience with AI frameworks (e.g., PyTorch, JAX) or distributed systems (e.g., Ray, Kubernetes)</li>\n<li>You have high engineering competence. 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All while releasing frontier models open-source, for everyone to try and benefit.</p>\n<p>What will you do?</p>\n<ul>\n<li>Research and develop novel methods to push the frontier of large language models</li>\n<li>Work across use cases (e.g reasoning, code, agents) and modalities (e.g text, image and speech)</li>\n<li>Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale</li>\n<li>Work cross-functionally with other scientists, engineers and product teams to ship AI systems which have a real-world impact</li>\n</ul>\n<p>About you</p>\n<ul>\n<li>An expert in speech input/output methodologies (specific to audio)</li>\n<li>You are a highly proficient software engineer in at least one programming language (Python or other, e.g. Rust, Go, Java)</li>\n<li>You have hands-on experience with AI frameworks (e.g. PyTorch, JAX) or distributed systems (e.g. Ray, Kubernetes)</li>\n<li>You have high engineering competence. This means being able to design complex software and make it usable in production</li>\n<li>You are a self-starter, autonomous and a team player</li>\n</ul>\n<p>Now, it would be ideal if</p>\n<ul>\n<li>You have experience working with large-scale speech-language models</li>\n<li>You have hands-on experience with training large transformer models in a distributed fashion</li>\n<li>You can navigate the full MLOps stack, for instance, fine-tuning, evaluation and deployment</li>\n<li>You have a strong publication record in a relevant scientific domain</li>\n</ul>\n<p>Note that this is not an exhaustive or necessary list of requirements. Please consider applying if you believe you have the skills to contribute to Mistral&#39;s mission. We value profile and experience diversity.</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_77b20c01-867","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Mistral","sameAs":"https://www.mistral.ai/"},"x-apply-url":"https://jobs.lever.co/mistral/94173e13-3050-4044-862a-e8dfc2deda5e","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"Competitive cash salary and equity","x-skills-required":["speech input/output methodologies","Python","PyTorch","JAX","distributed systems","Ray","Kubernetes","high engineering competence"],"x-skills-preferred":["large-scale speech-language models","training large transformer models in a distributed fashion","MLOps stack","fine-tuning","evaluation","deployment"],"datePosted":"2026-03-10T11:22:35.126Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Paris"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"speech input/output methodologies, Python, PyTorch, JAX, distributed systems, Ray, Kubernetes, high engineering competence, large-scale speech-language models, training large transformer models in a distributed fashion, MLOps stack, fine-tuning, evaluation, deployment"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9c72720b-6af"},"title":"Research Engineer, Science of Scaling","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>Anthropic is seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You&#39;ll contribute across the entire stack, from low-level optimizations to high-level algorithm and experimental design, balancing research goals with practical engineering constraints.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Conduct research into the science of converting compute into intelligence</li>\n<li>Independently lead small research projects while collaborating with team members on larger initiatives</li>\n<li>Design, run, and analyse scientific experiments to advance our understanding of large language models</li>\n<li>Optimise training infrastructure to improve efficiency and reliability</li>\n<li>Develop dev tooling to enhance team productivity</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 and a proven track record of building complex systems</li>\n<li>Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field</li>\n<li>Are proficient in Python and experienced with deep learning frameworks</li>\n<li>Are results-oriented with a bias towards flexibility and impact</li>\n<li>Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team</li>\n<li>View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximise impact</li>\n<li>Care about the societal impacts of your work and have ambitious goals for AI safety and general progress</li>\n</ul>\n<p><strong>Strong candidates may have:</strong></p>\n<ul>\n<li>Experience with JAX</li>\n<li>Experience with reinforcement learning</li>\n<li>Experience working on high-performance, large-scale ML systems</li>\n<li>Familiarity with accelerators, Kubernetes, and OS internals</li>\n<li>Experience with language modeling using transformer architectures</li>\n<li>Background in large-scale ETL processes</li>\n<li>Experience with distributed training at scale (thousands of accelerators)</li>\n</ul>\n<p><strong>Strong candidates need not have:</strong></p>\n<ul>\n<li>Experience in all of the above areas — we value breadth of interest and willingness to learn over checking every box</li>\n<li>Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well</li>\n<li>An advanced degree — exceptional engineers with strong research instincts are equally encouraged to apply</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 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</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_9c72720b-6af","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/5126127008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000 - £630,000GBP","x-skills-required":["software engineering","Python","deep learning frameworks","JAX","reinforcement learning","high-performance, large-scale ML systems","accelerators","Kubernetes","OS internals","language modeling using transformer architectures","large-scale ETL processes","distributed training at scale"],"x-skills-preferred":["JAX","reinforcement learning","high-performance, large-scale ML systems","accelerators","Kubernetes","OS internals","language modeling using transformer architectures","large-scale ETL processes","distributed training at scale"],"datePosted":"2026-03-08T13:50:55.750Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, Python, deep learning frameworks, JAX, reinforcement learning, high-performance, large-scale ML systems, accelerators, Kubernetes, OS internals, language modeling using transformer architectures, large-scale ETL processes, distributed training at scale, JAX, reinforcement learning, high-performance, large-scale ML systems, accelerators, Kubernetes, OS internals, language modeling using transformer architectures, large-scale ETL processes, distributed training at scale","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_601a3593-052"},"title":"Research Engineer, Machine Learning (Reinforcement Learning)","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>As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You&#39;ll work on fundamental research in reinforcement learning, creating &#39;agentic&#39; models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.</p>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.</li>\n<li>Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.</li>\n<li>Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.</li>\n<li>Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Are proficient in Python and async/concurrent programming with frameworks like Trio</li>\n<li>Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)</li>\n<li>Have industry experience in machine learning research</li>\n<li>Can balance research exploration with engineering implementation</li>\n<li>Enjoy pair programming (we love to pair!)</li>\n<li>Care about code quality, testing, and performance</li>\n<li>Have strong systems design and communication skills</li>\n<li>Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems</li>\n</ul>\n<p><strong>Strong candidates may have:</strong></p>\n<ul>\n<li>Familiarity with LLM architectures and training methodologies</li>\n<li>Experience with reinforcement learning techniques and environments</li>\n<li>Experience with virtualization and sandboxed code execution environments</li>\n<li>Experience with Kubernetes</li>\n<li>Experience with distributed systems or high-performance computing</li>\n<li>Experience with Rust and/or C++</li>\n</ul>\n<p><strong>Strong candidates need not have:</strong></p>\n<ul>\n<li>Formal certifications or education credentials</li>\n<li>Academic research experience or publication history</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. 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.</p>\n<p><strong>Your safety matters to us.</strong> To protect yourself from potential</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_601a3593-052","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/4613568008","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$500,000 - $850,000USD","x-skills-required":["Python","async/concurrent programming","Trio","PyTorch","TensorFlow","JAX","machine learning frameworks","reinforcement learning techniques","environments","virtualization","sandboxed code execution environments","Kubernetes","distributed systems","high-performance computing","Rust","C++"],"x-skills-preferred":["LLM architectures","training methodologies","reinforcement learning","distributed systems","high-performance computing"],"datePosted":"2026-03-08T13:49:41.142Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, async/concurrent programming, Trio, PyTorch, TensorFlow, JAX, machine learning frameworks, reinforcement learning techniques, environments, virtualization, sandboxed code execution environments, Kubernetes, distributed systems, high-performance computing, Rust, C++, LLM architectures, training methodologies, reinforcement learning, distributed systems, high-performance computing","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":500000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5d38ab71-400"},"title":"Research Engineer, Pretraining Scaling","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>Anthropic&#39;s ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company&#39;s future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you&#39;ll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.</p>\n<p>This role lives at the boundary between research and engineering. You&#39;ll work across our entire production training stack: performance optimisation, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can&#39;t wait for tomorrow.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Own critical aspects of our production pretraining pipeline, including model operations, performance optimisation, observability, and reliability</li>\n<li>Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure</li>\n<li>Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance</li>\n<li>Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams</li>\n<li>Build and maintain production logging, monitoring dashboards, and evaluation infrastructure</li>\n<li>Add new capabilities to the training codebase, such as long context support or novel architectures</li>\n<li>Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams</li>\n<li>Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned</li>\n</ul>\n<p><strong>You May Be a Good Fit If You:</strong></p>\n<ul>\n<li>Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems</li>\n<li>Genuinely enjoy both research and engineering work—you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other</li>\n<li>Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure</li>\n<li>Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs</li>\n<li>Excel at debugging complex, ambiguous problems across multiple layers of the stack</li>\n<li>Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents</li>\n<li>Are passionate about the work itself and want to refine your craft as a research engineer</li>\n<li>Care about the societal impacts of AI and responsible scaling</li>\n</ul>\n<p><strong>Strong Candidates May Also Have:</strong></p>\n<ul>\n<li>Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale</li>\n<li>Contributed to open-source LLM frameworks (e.g., open\\_lm, llm-foundry, mesh-transformer-jax)</li>\n<li>Published research on model training, scaling laws, or ML systems</li>\n<li>Experience with production ML systems, observability tools, or evaluation infrastructure</li>\n<li>Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence</li>\n</ul>\n<p><strong>What Makes This Role Unique:</strong></p>\n<p>This is not a typical research engineering role. The work is highly operational—you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.</p>\n<p>However, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.</p>\n<p>We&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI—and you&#39;re excited about the full reality of what that entails—we&#39;d love to hear from you.</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.</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_5d38ab71-400","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/4938432008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000USD","x-skills-required":["JAX","TPU","PyTorch","large-scale distributed systems","model operations","performance optimisation","observability","reliability","model training","scaling laws","ML systems"],"x-skills-preferred":["open-source LLM frameworks","production ML systems","observability tools","evaluation infrastructure","systems engineer","quant"],"datePosted":"2026-03-08T13:48:54.589Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"JAX, TPU, PyTorch, large-scale distributed systems, model operations, performance optimisation, observability, reliability, model training, scaling laws, ML systems, open-source LLM frameworks, production ML systems, observability tools, evaluation infrastructure, systems engineer, quant","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_4c433e22-526"},"title":"Privacy Research Engineer, Safeguards","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>We are looking for researchers to help mitigate the risks that come with building AI systems. One of these risks is the potential for models to interact with private user data. In this role, you&#39;ll design and implement privacy-preserving techniques, audit our current techniques, and set the direction for how Anthropic handles privacy more broadly.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Lead our privacy analysis of frontier models, carefully auditing the use of data and ensuring safety throughout the process</li>\n<li>Develop privacy-first training algorithms and techniques</li>\n<li>Develop evaluation and auditing techniques to measure the privacy of training algorithms</li>\n<li>Advocate on behalf of our users to ensure responsible handling of all data</li>\n</ul>\n<p><strong>You may be a good fit if you have:</strong></p>\n<ul>\n<li>Experience working on privacy-preserving machine learning</li>\n<li>A track record of shipping products and features inside a fast-moving environment</li>\n<li>Strong coding skills in Python and familiarity with ML frameworks like PyTorch or JAX.</li>\n<li>Deep familiarity with large language models, how they work, and how they are trained</li>\n<li>Have experience working with privacy-preserving techniques (e.g., differential privacy and how it is different from k-anonymity, l-diversity, and t-closeness)</li>\n<li>Experience supporting fast-paced startup engineering teams</li>\n<li>Demonstrated success in bringing clarity and ownership to ambiguous technical problems</li>\n<li>Proven ability to lead cross-functional security initiatives and navigate complex organizational dynamics</li>\n</ul>\n<p><strong>Strong candidates may also:</strong></p>\n<ul>\n<li>Have published papers on the topic of privacy-preserving ML at top academic venues</li>\n<li>Prior experience training large language models (e.g., collecting training datasets, pre-training models, post-training models via fine-tuning and RL, running evaluations on trained models)</li>\n<li>Prior experience developing tooling to support privacy-preserving ML (e.g., differential privacy in TF-Privacy or Opacus)</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.</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 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><strong>Come work with us!</strong></p>\n<p>Anthropic 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_4c433e22-526","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/4949108008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000 - $485,000 USD","x-skills-required":["Python","PyTorch","JAX","Machine Learning","Differential Privacy","k-anonymity","l-diversity","t-closeness"],"x-skills-preferred":["Large Language Models","Training Algorithms","Evaluation and Auditing Techniques","Cross-functional Security Initiatives"],"datePosted":"2026-03-08T13:46:51.956Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, JAX, Machine Learning, Differential Privacy, k-anonymity, l-diversity, t-closeness, Large Language Models, Training Algorithms, Evaluation and Auditing Techniques, Cross-functional Security Initiatives","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_58928a28-64d"},"title":"Research Engineer/Research Scientist, Audio","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>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have hands-on experience with training audio models, whether that&#39;s conversational speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, or generative audio models</li>\n<li>Genuinely enjoy both research and engineering work, and you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other</li>\n<li>Are comfortable working across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization</li>\n<li>Have deep expertise with JAX, PyTorch, or large-scale distributed training, and can debug performance issues across the full stack</li>\n<li>Thrive in fast-moving environments where the most important problem might shift as we learn more about what works</li>\n<li>Communicate clearly and collaborate effectively; audio touches many parts of our systems, so you&#39;ll work closely with teams across the company</li>\n<li>Are passionate about building conversational AI that feels natural, steerable, and safe</li>\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<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>Large language model pretraining and finetuning</li>\n<li>Training diffusion models for image and audio generation</li>\n<li>Reinforcement learning for large language models and diffusion models</li>\n<li>End-to-end system optimization, from performance benchmarking to kernel optimization</li>\n<li>GPUs, Kubernetes, PyTorch, or distributed training infrastructure</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Training state-of-the art neural audio codecs for 48 kHz stereo audio</li>\n<li>Developing novel algorithms for diffusion pretraining and reinforcement learning</li>\n<li>Scaling audio datasets to millions of hours of high quality audio</li>\n<li>Creating robust evaluation methodologies for hard-to-measure qualities such as naturalness or expressiveness</li>\n<li>Studying training dynamics of mixed audio-text language models</li>\n<li>Optimizing latency and inference throughput for deployed streaming audio 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><strong>Your safety matters to us.</strong> 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><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 systems that benefit society.</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_58928a28-64d","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/5074815008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $500,000 USD","x-skills-required":["audio models","speech-to-speech","speech translation","speech recognition","text-to-speech","diarization","codecs","generative audio models","JAX","PyTorch","large-scale distributed training"],"x-skills-preferred":["large language model pretraining","training diffusion models","reinforcement learning","end-to-end system optimization","GPUs","Kubernetes","PyTorch","distributed training infrastructure"],"datePosted":"2026-03-08T13:46:24.550Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"audio models, speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, generative audio models, JAX, PyTorch, large-scale distributed training, large language model pretraining, training diffusion models, reinforcement learning, end-to-end system optimization, GPUs, Kubernetes, PyTorch, distributed training infrastructure","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":500000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6cc383e0-ff6"},"title":"ML Infrastructure Engineer, Safeguards","description":"<p><strong>About the role</strong></p>\n<p>We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you&#39;ll build and scale the critical infrastructure that powers our AI safety systems. You&#39;ll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem</li>\n<li>Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications</li>\n<li>Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems</li>\n<li>Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards</li>\n<li>Implement automated testing, deployment, and rollback systems for ML models in production safety applications</li>\n<li>Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs</li>\n<li>Contribute to the development of internal tools and frameworks that accelerate safety research and deployment</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment</li>\n<li>Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX</li>\n<li>Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)</li>\n<li>Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads</li>\n<li>Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)</li>\n<li>Are results-oriented, with a bias towards reliability and impact in safety-critical systems</li>\n<li>Enjoy collaborating with researchers and translating cutting-edge research into production systems</li>\n<li>Care deeply about AI safety and the societal impacts of your work</li>\n</ul>\n<p><strong>Strong candidates may have experience with:</strong></p>\n<ul>\n<li>Working with large language models and modern transformer architectures</li>\n<li>Implementing A/B testing frameworks and experimentation infrastructure for ML systems</li>\n<li>Developing monitoring and alerting systems for ML model performance and data drift</li>\n<li>Building automated labeling systems and human-in-the-loop workflows</li>\n<li>Experience in trust &amp; safety, fraud prevention, or content moderation domains</li>\n<li>Knowledge of privacy-preserving ML techniques and compliance requirements</li>\n<li>Contributing to open-source ML infrastructure projects</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<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.</strong></p>\n<p>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><strong>Your safety matters to us.</strong></p>\n<p>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><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 the state of the art in AI safety and making a meaningful difference in 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_6cc383e0-ff6","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/4778843008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000 - $405,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","JAX","AWS","GCP","Kubernetes","Spark","Airflow","streaming systems"],"x-skills-preferred":["large language models","modern transformer architectures","A/B testing frameworks","experimentation infrastructure","monitoring and alerting systems","automated labeling systems","human-in-the-loop workflows","trust & safety","fraud prevention","content moderation domains","privacy-preserving ML techniques","compliance requirements"],"datePosted":"2026-03-08T13:46:05.401Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, JAX, AWS, GCP, Kubernetes, Spark, Airflow, streaming systems, large language models, modern transformer architectures, A/B testing frameworks, experimentation infrastructure, monitoring and alerting systems, automated labeling systems, human-in-the-loop workflows, trust & safety, fraud prevention, content moderation domains, privacy-preserving ML techniques, compliance requirements","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_11a60d5a-f54"},"title":"Performance Engineer, GPU","description":"<p><strong>About the role:</strong></p>\n<p>Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. As a GPU Performance Engineer, you&#39;ll architect and implement the foundational systems that power Claude and push the frontiers of what&#39;s possible with large language models. You&#39;ll be responsible for maximizing GPU utilization and performance at unprecedented scale, developing cutting-edge optimizations that directly enable new model capabilities and dramatically improve inference efficiency.</p>\n<p>Working at the intersection of hardware and software, you&#39;ll implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack—from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.</p>\n<p>Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.</p>\n<p><strong>You might be a good fit if you:</strong></p>\n<ul>\n<li>Have deep experience with GPU programming and optimization at scale</li>\n<li>Are impact-driven, passionate about delivering measurable performance breakthroughs</li>\n<li>Can navigate complex systems from hardware interfaces to high-level ML frameworks</li>\n<li>Enjoy collaborative problem-solving and pair programming</li>\n<li>Want to work on state-of-the-art language models with real-world impact</li>\n<li>Care about the societal impacts of your work</li>\n<li>Thrive in ambiguous environments where you define the path forward</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization</li>\n<li>ML Compilers &amp; Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators</li>\n<li>Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight</li>\n<li>Distributed Systems: NCCL, NVLink, collective communication, model parallelism</li>\n<li>Low-Precision: INT8/FP8 quantization, mixed-precision techniques</li>\n<li>Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Co-design attention mechanisms and algorithms for next-generation hardware architectures</li>\n<li>Develop custom kernels for emerging quantization formats and mixed-precision techniques</li>\n<li>Design distributed communication strategies for multi-node GPU clusters</li>\n<li>Optimize end-to-end training and inference pipelines for frontier language models</li>\n<li>Build performance modeling frameworks to predict and optimize GPU utilization</li>\n<li>Implement kernel fusion strategies to minimize memory bandwidth bottlenecks</li>\n<li>Create resilient systems for planet-scale distributed training infrastructure</li>\n<li>Profile and eliminate performance bottlenecks in production serving infrastructure</li>\n<li>Partner with hardware vendors to influence future accelerator capabilities and software stacks</li>\n</ul>\n<p><strong>Deadline to apply:</strong> None. Applications will be reviewed on a rolling basis.</p>\n<p>The expected salary range for this position is:</p>\n<p>Annual Salary: $280,000 - $850,000USD</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_11a60d5a-f54","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/4926227008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$280,000 - $850,000USD","x-skills-required":["GPU programming","optimization at scale","custom kernel development","distributed system architectures","low-level tensor core optimizations","orchestrating thousands of GPUs","GPU kernel development","CUDA","Triton","CUTLASS","Flash Attention","tensor core optimization","ML compilers & frameworks","PyTorch/JAX internals","torch.compile","XLA","custom operators","performance engineering","kernel fusion","memory bandwidth optimization","profiling with Nsight","distributed systems","NCCL","NVLink","collective communication","model parallelism","low-precision","INT8/FP8 quantization","mixed-precision techniques","production systems","large-scale training infrastructure","fault tolerance","cluster orchestration"],"x-skills-preferred":["GPU programming","optimization at scale","custom kernel development","distributed system architectures","low-level tensor core optimizations","orchestrating thousands of GPUs","GPU kernel development","CUDA","Triton","CUTLASS","Flash Attention","tensor core optimization","ML compilers & frameworks","PyTorch/JAX internals","torch.compile","XLA","custom operators","performance engineering","kernel fusion","memory bandwidth optimization","profiling with Nsight","distributed systems","NCCL","NVLink","collective communication","model parallelism","low-precision","INT8/FP8 quantization","mixed-precision techniques","production systems","large-scale training infrastructure","fault tolerance","cluster orchestration"],"datePosted":"2026-03-08T13:45:05.412Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"GPU programming, optimization at scale, custom kernel development, distributed system architectures, low-level tensor core optimizations, orchestrating thousands of GPUs, GPU kernel development, CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization, ML compilers & frameworks, PyTorch/JAX internals, torch.compile, XLA, custom operators, performance engineering, kernel fusion, memory bandwidth optimization, profiling with Nsight, distributed systems, NCCL, NVLink, collective communication, model parallelism, low-precision, INT8/FP8 quantization, mixed-precision techniques, production systems, large-scale training infrastructure, fault tolerance, cluster orchestration, GPU programming, optimization at scale, custom kernel development, distributed system architectures, low-level tensor core optimizations, orchestrating thousands of GPUs, GPU kernel development, CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization, ML compilers & frameworks, PyTorch/JAX internals, torch.compile, XLA, custom operators, performance engineering, kernel fusion, memory bandwidth optimization, profiling with Nsight, distributed systems, NCCL, NVLink, collective communication, model parallelism, low-precision, INT8/FP8 quantization, mixed-precision techniques, production systems, large-scale training infrastructure, fault tolerance, cluster orchestration","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":280000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_221e855f-2b9"},"title":"Research Engineer, Machine Learning (Reinforcement Learning)","description":"<p><strong>About the Role</strong></p>\n<p>As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You&#39;ll work on fundamental research in reinforcement learning, creating &#39;agentic&#39; models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.</p>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.</li>\n</ul>\n<ul>\n<li>Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.</li>\n</ul>\n<ul>\n<li>Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.</li>\n</ul>\n<ul>\n<li>Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Are proficient in Python and async/concurrent programming with frameworks like Trio</li>\n</ul>\n<ul>\n<li>Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)</li>\n</ul>\n<ul>\n<li>Have industry experience in machine learning research</li>\n</ul>\n<ul>\n<li>Can balance research exploration with engineering implementation</li>\n</ul>\n<ul>\n<li>Enjoy pair programming (we love to pair!)</li>\n</ul>\n<ul>\n<li>Care about code quality, testing, and performance</li>\n</ul>\n<ul>\n<li>Have strong systems design and communication skills</li>\n</ul>\n<ul>\n<li>Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems</li>\n</ul>\n<p><strong>Strong candidates may have:</strong></p>\n<ul>\n<li>Familiarity with LLM architectures and training methodologies</li>\n</ul>\n<ul>\n<li>Experience with reinforcement learning techniques and environments</li>\n</ul>\n<ul>\n<li>Experience with virtualization and sandboxed code execution environments</li>\n</ul>\n<ul>\n<li>Experience with Kubernetes</li>\n</ul>\n<ul>\n<li>Experience with distributed systems or high-performance computing</li>\n</ul>\n<ul>\n<li>Experience with Rust and/or C++</li>\n</ul>\n<p><strong>Strong candidates need not have:</strong></p>\n<ul>\n<li>Formal certifications or education credentials</li>\n</ul>\n<ul>\n<li>Academic research experience or publication history</li>\n</ul>\n<p><strong>Deadline to apply:</strong> None. Applications will be reviewed on a rolling basis.</p>\n<p>The annual compensation range for this role is listed below.</p>\n<p>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.</p>\n<p>Annual Salary:</p>\n<p>£260,000 - £630,000GBP</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.</p>\n<p><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. 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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><strong>Your safety matters to us.</strong> To protect yourself from potential scams, remember that Anthropic is a legitimate company and we will never ask you to pay any fees or provide sensitive information via email or phone.</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_221e855f-2b9","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/5115935008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000 - £630,000GBP","x-skills-required":["Python","async/concurrent programming","Trio","machine learning frameworks","PyTorch","TensorFlow","JAX","reinforcement learning techniques","environments","virtualization","sandboxed code execution environments","Kubernetes","distributed systems","high-performance computing","Rust","C++"],"x-skills-preferred":["LLM architectures","training methodologies","reinforcement learning techniques","environments","virtualization","sandboxed code execution environments","Kubernetes","distributed systems","high-performance computing","Rust","C++"],"datePosted":"2026-03-08T13:44:26.776Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, async/concurrent programming, Trio, machine learning frameworks, PyTorch, TensorFlow, JAX, reinforcement learning techniques, environments, virtualization, sandboxed code execution environments, Kubernetes, distributed systems, high-performance computing, Rust, C++, LLM architectures, training methodologies, reinforcement learning techniques, environments, virtualization, sandboxed code execution environments, Kubernetes, distributed systems, high-performance computing, Rust, C++","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a05bfa1a-d23"},"title":"Research Engineer, Pretraining Scaling","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>Anthropic&#39;s ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company&#39;s future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you&#39;ll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.</p>\n<p>This role lives at the boundary between research and engineering. You&#39;ll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can&#39;t wait for tomorrow.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability</li>\n<li>Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure</li>\n<li>Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance</li>\n<li>Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams</li>\n<li>Build and maintain production logging, monitoring dashboards, and evaluation infrastructure</li>\n<li>Add new capabilities to the training codebase, such as long context support or novel architectures</li>\n<li>Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams</li>\n<li>Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned</li>\n</ul>\n<p><strong>You May Be a Good Fit If You:</strong></p>\n<ul>\n<li>Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems</li>\n<li>Genuinely enjoy both research and engineering work—you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other</li>\n<li>Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure</li>\n<li>Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs</li>\n<li>Excel at debugging complex, ambiguous problems across multiple layers of the stack</li>\n<li>Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents</li>\n<li>Are passionate about the work itself and want to refine your craft as a research engineer</li>\n<li>Care about the societal impacts of AI and responsible scaling</li>\n</ul>\n<p><strong>Strong Candidates May Also Have:</strong></p>\n<ul>\n<li>Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale</li>\n<li>Contributed to open-source LLM frameworks (e.g., open\\_lm, llm-foundry, mesh-transformer-jax)</li>\n<li>Published research on model training, scaling laws, or ML systems</li>\n<li>Experience with production ML systems, observability tools, or evaluation infrastructure</li>\n<li>Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence</li>\n</ul>\n<p><strong>What Makes This Role Unique:</strong></p>\n<p>This is not a typical research engineering role. The work is highly operational—you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.</p>\n<p>However, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.</p>\n<p>We&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI—and you&#39;re excited about the full reality of what that entails—we&#39;d love to hear from you.</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 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_a05bfa1a-d23","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/4938436008","x-work-arrangement":"onsite","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"£260,000 - £630,000GBP","x-skills-required":["JAX","TPU","PyTorch","large-scale distributed systems","model operations","performance optimization","observability","reliability","debugging","experimental design","launch coordination","production logging","monitoring dashboards","evaluation infrastructure","collaboration","communication"],"x-skills-preferred":["open-source LLM frameworks","research on model training","scaling laws","ML systems","production ML systems","observability tools","evaluation infrastructure","systems engineering","quant","operational excellence"],"datePosted":"2026-03-08T13:44:15.893Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"JAX, TPU, PyTorch, large-scale distributed systems, model operations, performance optimization, observability, reliability, debugging, experimental design, launch coordination, production logging, monitoring dashboards, evaluation infrastructure, collaboration, communication, open-source LLM frameworks, research on model training, scaling laws, ML systems, production ML systems, observability tools, evaluation infrastructure, systems engineering, quant, operational excellence","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_2bfc37e4-bc3"},"title":"Researcher, Pretraining Safety","description":"<p><strong>Job Posting</strong></p>\n<p><strong>Researcher, Pretraining Safety</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>Department</strong></p>\n<p>Safety Systems</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$295K – $445K • 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<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>More details about our benefits are available to candidates during the hiring process.</p>\n<p>This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.</p>\n<p><strong><strong>About the Team</strong></strong></p>\n<p>The Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI&#39;s mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency.</p>\n<p>The Pretraining Safety team’s goal is to build safer, more capable base models and enable earlier, more reliable safety evaluation during training. We aim to:</p>\n<ol>\n<li><strong>Develop upstream safety evaluations</strong> that to monitor how and when unsafe behaviors and goals emerge;</li>\n</ol>\n<ol>\n<li><strong>Create safer priors</strong> through targeted pretraining and mid-training interventions that make downstream alignment more effective and efficient</li>\n</ol>\n<ol>\n<li><strong>Design safe-by-design architectures</strong> that allow for more controllability of model capabilities</li>\n</ol>\n<p>In addition, we will conduct the foundational research necessary for understanding how behaviors emerge, generalize, and can be reliably measured throughout training.</p>\n<p><strong><strong>About the Role</strong></strong></p>\n<p>The Pretraining Safety team is pioneering how safety is built into models before they reach post-training and deployment. In this role, you will work throughout the full stack of model development with a focus on pre-training:</p>\n<ul>\n<li>Identify safety-relevant behaviors as they first emerge in base models</li>\n</ul>\n<ul>\n<li>Evaluate and reduce risk without waiting for full-scale training runs</li>\n</ul>\n<ul>\n<li>Design architectures and training setups that make safer behavior the default</li>\n</ul>\n<ul>\n<li>Strengthen models by incorporating richer, earlier safety signals</li>\n</ul>\n<p>We collaborate across OpenAI’s safety ecosystem—from Safety Systems to Training—to ensure that safety foundations are robust, scalable, and grounded in real-world risks.</p>\n<p><strong><strong>In this role, you will:</strong></strong></p>\n<ul>\n<li>Develop new techniques to predict, measure, and evaluate unsafe behavior in early-stage models</li>\n</ul>\n<ul>\n<li>Design data curation strategies that improve pretraining priors and reduce downstream risk</li>\n</ul>\n<ul>\n<li>Explore safe-by-design architectures and training configurations that improve controllability</li>\n</ul>\n<ul>\n<li>Introduce novel safety-oriented loss functions, metrics, and evals into the pretraining stack</li>\n</ul>\n<ul>\n<li>Work closely with cross-functional safety teams to unify pre- and post-training risk reduction</li>\n</ul>\n<p><strong><strong>You might thrive in this role if you:</strong></strong></p>\n<ul>\n<li>Have experience developing or scaling pretraining architectures (LLMs, diffusion models, multimodal models, etc.)</li>\n</ul>\n<ul>\n<li>Are comfortable working with training infrastructure, data pipelines, and evaluation frameworks (e.g., Python, PyTorch/JAX, Apache Beam)</li>\n</ul>\n<ul>\n<li>Enjoy hands-on research — designing, implementing, and iterating on experiments</li>\n</ul>\n<ul>\n<li>Enjoy collaborating with diverse technical and cross-functional partners (e.g., policy, legal, training)</li>\n</ul>\n<ul>\n<li>Are data-driven with strong statistical reasoning and rigor in experimental design</li>\n</ul>\n<ul>\n<li>Value building clean, scalable research workflows and streamlining processes for yourself and others</li>\n</ul>\n<p><strong><strong>About OpenAI</strong></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_2bfc37e4-bc3","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/d829b701-5ee2-414f-8596-ef94911a168a","x-work-arrangement":"onsite","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$295K – $445K • Offers Equity","x-skills-required":["pretraining architectures","training infrastructure","data pipelines","evaluation frameworks","Python","PyTorch/JAX","Apache Beam","hands-on research","collaboration","data-driven","statistical reasoning"],"x-skills-preferred":["LLMs","diffusion models","multimodal models","safe-by-design architectures","training configurations","loss functions","metrics","evals"],"datePosted":"2026-03-06T18:36:25.493Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"pretraining architectures, training infrastructure, data pipelines, evaluation frameworks, Python, PyTorch/JAX, Apache Beam, hands-on research, collaboration, data-driven, statistical reasoning, LLMs, diffusion models, multimodal models, safe-by-design architectures, training configurations, loss functions, metrics, evals","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":295000,"maxValue":445000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_0e457a06-cee"},"title":"Training Performance Engineer","description":"<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>Scaling</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$250K – $445K • 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<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>More details about our benefits are available to candidates during the hiring process.</p>\n<p>This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.</p>\n<p><strong>About the Team</strong> Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve.</p>\n<p>Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes.</p>\n<p>We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products).</p>\n<p><strong>About the Role</strong> As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale.</p>\n<p>You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget.</p>\n<p>This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Profile end-to-end training runs to identify performance bottlenecks across compute, communication, and storage.</li>\n<li>Optimize GPU utilization and throughput for large-scale distributed model training.</li>\n<li>Collaborate with runtime and systems engineers to improve kernel efficiency, scheduling, and collective communication performance.</li>\n<li>Implement model graph transforms to improve end to end throughput.</li>\n<li>Build tooling to monitor and visualize MFU, throughput, and uptime across clusters.</li>\n<li>Partner with researchers to ensure new model architectures scale efficiently during pre-training.</li>\n<li>Contribute to infrastructure decisions that improve reliability and efficiency of large training jobs.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Love optimizing performance and digging into systems to understand how every layer interacts.</li>\n<li>Have strong programming skills in Python and C++ (Rust or CUDA a plus).</li>\n<li>Have experience running distributed training jobs on multi-GPU systems or HPC clusters.</li>\n<li>Enjoy debugging complex distributed systems and measuring efficiency rigorously.</li>\n<li>Have exposure to frameworks like PyTorch, JAX, or TensorFlow and an understanding of how large-scale training loops are built.</li>\n<li>Are comfortable collaborating across teams and translating raw profiling data into practical engineering improvements.</li>\n</ul>\n<p><strong>Nice to have:</strong></p>\n<ul>\n<li>Familiarity with NCCL, MPI, or UCX communication libraries.</li>\n<li>Experience with large-scale data loading and checkpointing systems.</li>\n<li>Prior work on training runtime, distributed scheduling, or ML compiler optimization.</li>\n</ul>\n<p><strong>About OpenAI</strong> 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</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_0e457a06-cee","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/6eb386ac-9056-4795-aa79-a27e105faf5c","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$250K – $445K","x-skills-required":["Python","C++","Rust","CUDA","PyTorch","JAX","TensorFlow","NCCL","MPI","UCX"],"x-skills-preferred":["Large-scale data loading and checkpointing systems","Training runtime, distributed scheduling, or ML compiler optimization"],"datePosted":"2026-03-06T18:32:30.509Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, C++, Rust, CUDA, PyTorch, JAX, TensorFlow, NCCL, MPI, UCX, Large-scale data loading and checkpointing systems, Training runtime, distributed scheduling, or ML compiler optimization","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":445000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_126e68f6-5ef"},"title":"Research Engineer, Privacy","description":"<p><strong>Job Posting</strong></p>\n<p><strong>Research Engineer, Privacy</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>Security</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$380K – $445K • 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<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>More details about our benefits are available to candidates during the hiring process.</p>\n<p>This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.</p>\n<p><strong>About the Team</strong></p>\n<p>The Privacy Engineering Team at OpenAI is committed to integrating privacy as a foundational element in OpenAI&#39;s mission of advancing Artificial General Intelligence (AGI). Our focus is on all OpenAI products and systems handling user data, striving to uphold the highest standards of data privacy and security.</p>\n<p>We build essential production services, develop novel privacy-preserving techniques, and equip cross-functional engineering and research partners with the necessary tools to ensure responsible data use. Our approach to prioritizing responsible data use is integral to OpenAI&#39;s mission of safely introducing AGI that offers widespread benefits.</p>\n<p><strong>About the Role</strong></p>\n<p>As a part of the Privacy Engineering Team, you will work on the frontlines of safeguarding user data while ensuring the usability and efficiency of our AI systems. You will help us understand and implement the latest research in privacy-enhancing technologies such as differential privacy, federated learning, and data memorization. Moreover, you will focus on investigating the interaction between privacy and machine learning, developing innovative techniques to improve data anonymization, and preventing model inversion and membership inference attacks.</p>\n<p><strong>This position is located in San Francisco. Relocation assistance is available.</strong></p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Design and prototype privacy-preserving machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning) that can be deployed at OpenAI scale.</li>\n</ul>\n<ul>\n<li>Measure and strengthen model robustness against privacy attacks such as membership inference, model inversion, and data memorization leaks—balancing utility with provable guarantees.</li>\n</ul>\n<ul>\n<li>Develop internal libraries, evaluation suites, and documentation that make cutting-edge privacy techniques accessible to engineering and research teams.</li>\n</ul>\n<ul>\n<li>Lead deep-dive investigations into the privacy–performance trade-offs of large models, publishing insights that inform model-training and product-safety decisions.</li>\n</ul>\n<ul>\n<li>Define and codify privacy standards, threat models, and audit procedures that guide the entire ML lifecycle—from dataset curation to post-deployment monitoring.</li>\n</ul>\n<ul>\n<li>Collaborate across Security, Policy, Product, and Legal to translate evolving regulatory requirements into practical technical safeguards and tooling.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Have hands-on research or production experience with PETs.</li>\n</ul>\n<ul>\n<li>Are fluent in modern deep-learning stacks (PyTorch/JAX) and comfortable turning cutting-edge papers into reliable, well-tested code.</li>\n</ul>\n<ul>\n<li>Enjoy stress-testing models—probing them for private data leakage—and can explain complex attack vectors to non-experts with clarity.</li>\n</ul>\n<ul>\n<li>Have a track record of publishing (or implementing) novel privacy or security work and relish bridging the gap between academia and real-world systems.</li>\n</ul>\n<ul>\n<li>Thrive in fast-moving, cross-disciplinary environments where you alternate between open-ended research and shipping production features under tight deadlines.</li>\n</ul>\n<ul>\n<li>Communicate crisply, document rigorously, and care deeply about building AI systems that respect user privacy while pushing the frontiers of capability.</li>\n</ul>\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_126e68f6-5ef","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/cc434f5b-dc0b-42fd-97ec-e0171545c6e9","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$380K – $445K","x-skills-required":["differential privacy","federated learning","data memorization","PyTorch","JAX","machine learning","security","policy","product","legal"],"x-skills-preferred":["novel privacy or security work","cross-disciplinary environments","open-ended research","shipping production features"],"datePosted":"2026-03-06T18:30:30.582Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"differential privacy, federated learning, data memorization, PyTorch, JAX, machine learning, security, policy, product, legal, novel privacy or security work, cross-disciplinary environments, open-ended research, shipping production features","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":380000,"maxValue":445000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_46bb9922-091"},"title":"ML Research Engineer - Hardware Codesign","description":"<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>Department</strong></p>\n<p>Scaling</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$185K – $455K • 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<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>More details about our benefits are available to candidates during the hiring process.</p>\n<p>This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.</p>\n<p><strong><strong>About the Team</strong></strong></p>\n<p>OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI.</p>\n<p><strong><strong>About the Role</strong></strong></p>\n<p>We’re seeking a Research-Hardware Codesign Engineer to operate at the boundary between model research and silicon/system architecture. You’ll help shape the numerics, architecture, and technology bets of future OpenAI silicon in collaboration with both Research and Hardware.</p>\n<p>Your work will include debugging gaps between rooflines and reality, writing quantization kernels, derisking numerics via model evals, quantifying system architecture tradeoffs, and implementing novel numeric RTL. This is a hands-on role for people who go looking for hard problems, get to ground truth, and drive it to production. Strong prioritization and clear, honest communication are essential.</p>\n<p>Location: San Francisco, CA (Hybrid: 3 days/week onsite)</p>\n<p>Relocation assistance available.</p>\n<p><strong><strong>In this role you will:</strong></strong></p>\n<ul>\n<li>Build on our roofline simulator to track evolving workloads, and deliver analyses that quantify the impact of system architecture decisions and support technology pathfinding.</li>\n</ul>\n<ul>\n<li>Debug gaps between performance simulation and real measurements; clearly communicate root cause, bottlenecks, and invalid assumptions.</li>\n</ul>\n<ul>\n<li>Write emulation kernels for low-precision numerics and lossy compression schemes, and get Research the information they need to trade efficiency with model quality.</li>\n</ul>\n<ul>\n<li>Prototype numerics modules by pushing RTL through synthesis; hand off novel numerics cleanly, or occasionally own an RTL module end-to-end.</li>\n</ul>\n<ul>\n<li>Proactively pull in new ML workloads, prototype them with rooflines and/or functional simulation, and drive initial evaluation of new opportunities or risks.</li>\n</ul>\n<ul>\n<li>Understand the whole picture from ML science to hardware optimization, and slice this end-to-end objective into near-term deliverables.</li>\n</ul>\n<ul>\n<li>Build ad-hoc collaborations across teams with very different goals and areas of expertise, and keep progress unblocked.</li>\n</ul>\n<ul>\n<li>Communicate design tradeoffs clearly with explicit assumptions and confidence levels; produce a trail of evidence that enables confident execution.</li>\n</ul>\n<p><strong><strong>You Will Thrive in this Role if:</strong></strong></p>\n<ul>\n<li>An exceptional track record of high-quality technical output, and a bias for shipping a prototype now and iterating later in the absence of clear requirements.</li>\n</ul>\n<ul>\n<li>Strong Python, and C++ or Rust, with a cautious attitude toward correctness and an intuition for clean extensibility.</li>\n</ul>\n<ul>\n<li>Experience writing Triton, CUDA, or similar, and an understanding of the resulting mapping of tensor ops to functional units.</li>\n</ul>\n<ul>\n<li>Working knowledge of PyTorch or JAX; experience in large ML codebases is a plus.</li>\n</ul>\n<ul>\n<li>Practical understanding of floating point numerics, the ML tradeoffs of reduced precision, and the current state of the art in model quantization.</li>\n</ul>\n<ul>\n<li>Deep understanding of transformer models, and strong intuition for transformer rooflines and the tradeoffs of sharded training and inference in large-scale ML systems.</li>\n</ul>\n<ul>\n<li>Experience writing RTL (especially for floating point logic) and understanding of PPA tradeoffs is a plus.</li>\n</ul>\n<ul>\n<li>Strong cross-functional communication (e.g. across ML researchers and hardware engineers); ability to slice ambiguous early-incubation ideas into concrete arenas in which progress can be made.</li>\n</ul>\n<p>_To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements.</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_46bb9922-091","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/5931abef-191b-417e-89f1-1d06f00e908c","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$185K – $455K","x-skills-required":["Python","C++","Rust","Triton","CUDA","PyTorch","JAX","Floating point numerics","Model quantization","Transformer models","RTL","PPA tradeoffs"],"x-skills-preferred":["Strong Python","C++ or Rust","Experience writing Triton","CUDA or similar","Working knowledge of PyTorch or JAX","Experience in large ML codebases"],"datePosted":"2026-03-06T18:28:06.437Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, C++, Rust, Triton, CUDA, PyTorch, JAX, Floating point numerics, Model quantization, Transformer models, RTL, PPA tradeoffs, Strong Python, C++ or Rust, Experience writing Triton, CUDA or similar, Working knowledge of PyTorch or JAX, Experience in large ML codebases","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":185000,"maxValue":455000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_e13425ae-b83"},"title":"Senior Machine Learning Engineer, Multimodal AI, Computer Vision and Graphics - PhD Early Career","description":"<p>Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.</p>\n<p>At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.</p>\n<p>A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.</p>\n<p>At Roblox, computer vision and graphics power the way our global community discovers, creates, and interacts on our virtual platform. This role involves building cutting-edge models to interpret and analyze every form of content—experiences, text, images, videos, and 3D avatars. Your work will directly influence core systems that drive the next generation of creation, search, recommendations, and trust &amp; safety initiatives across our massive ecosystem.</p>\n<p>We’re looking for PhD candidates passionate about the intersection of computer vision, graphics, and generative modeling. You’ll work on applied research and engineering projects with direct production impact — enabling new ways for creators and players to bring ideas to life.</p>\n<p><strong>Teams Hiring for This Role</strong></p>\n<ul>\n<li><strong>Content Understanding:</strong> Develop large-scale computer vision and multimodal models that classify, organize, and recommend 3D and visual content to improve user discovery, personalization, and safety.</li>\n</ul>\n<ul>\n<li><strong>Account Identity</strong> : focuses on various aspects of user Identity from bot detection, alternate account detection graph, to age estimation based on behavioral and facial features.</li>\n</ul>\n<p><strong>You Will:</strong></p>\n<ul>\n<li>Lead the research and development of deep learning models for visual content understanding and 3D content generation (image/video/3D scenes, avatars, and assets).</li>\n</ul>\n<ul>\n<li>Design and implement foundation models for visual and 3D-based creation, search, and recommendations, ensuring a high level of fidelity, relevance, and ranking.</li>\n</ul>\n<ul>\n<li>Break down complex product requirements into iterative deliverable stages, moving applied research into high-scale production systems.</li>\n</ul>\n<ul>\n<li>Implement innovative visual and multi-modal models that power core Roblox functions (e.g., world creation, avatar systems, search, and recommendations).</li>\n</ul>\n<ul>\n<li>Build high precision facial age estimation across demographics from ground up including various fraud detection techniques for a robust and safe user identity system.</li>\n</ul>\n<p><strong>You Have:</strong></p>\n<ul>\n<li>Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.</li>\n</ul>\n<ul>\n<li>Expertise in one or more areas: computer vision, multimodal learning, 3D Graphics, or large-scale representation learning.</li>\n</ul>\n<ul>\n<li>Experience developing and training deep learning models using modern frameworks (PyTorch, TensorFlow, JAX).</li>\n</ul>\n<ul>\n<li>A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues.</li>\n</ul>\n<ul>\n<li>Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.</li>\n</ul>\n<p>You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.</p>\n<p>As you apply, you can find more information about our process by signing up for Speak\\_. You&#39;ll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.</p>\n<p>For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.</p>\n<p>Annual Salary Range</p>\n<p>$195,780—$242,100 USD</p>\n<p>Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).</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_e13425ae-b83","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Roblox","sameAs":"https://careers.roblox.com","logo":"https://logos.yubhub.co/careers.roblox.com.png"},"x-apply-url":"https://careers.roblox.com/jobs/7323437","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$195,780—$242,100 USD","x-skills-required":["computer vision","multimodal learning","3D Graphics","large-scale representation learning","PyTorch","TensorFlow","JAX","Python","C++","Go","Java"],"x-skills-preferred":[],"datePosted":"2026-03-06T14:19:13.911Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Mateo, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"computer vision, multimodal learning, 3D Graphics, large-scale representation learning, PyTorch, TensorFlow, JAX, Python, C++, Go, Java","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":195780,"maxValue":242100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_146f30de-73a"},"title":"Principal Applied Scientist","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Principal Applied Scientist at their Beijing office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that&#39;s revolutionising the field of AI. You&#39;ll work directly with leadership to shape the company&#39;s direction in the AI market.</p>\n<p><strong>About the Role</strong></p>\n<p>We are seeking an Applied Scientist / AI Architect with strong hands-on experience in building and optimizing large language models (LLMs), agentic AI systems, and end-to-end model training workflows. This role is ideal for scientists with a solid applied background who can translate state-of-the-art research into real-world impact. 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This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that&#39;s revolutionising the AI and engineering system. You&#39;ll work directly with leadership to shape the company&#39;s direction in the global Office users market.</p>\n<p><strong>About the Role</strong></p>\n<p>We are seeking a highly skilled and motivated Applied Scientist with strong hands-on experience in building and optimizing agentic AI systems. Our mission is to benefit Office users with rich content and tool support to raise productivity, and we are building the AI and engineering system to make it happen. This position offers an exciting opportunity to design and develop highly complex and comprehensive systems combining engineering, AI and human participation. 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