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We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.</p>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP)</li>\n<li>Python or Rust</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p>Representative projects across the org:</p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p>Deadline to apply: None. Applications will be reviewed on a rolling basis.</p>\n<p>The annual compensation range for this role is £225,000-£325,000 GBP.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_61e346b2-915","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5152348008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£225,000-£325,000 GBP","x-skills-required":["High-performance, large-scale distributed systems","Implementing and deploying machine learning systems at scale","Load balancing, request routing, or traffic management systems","LLM inference optimization, batching, and caching strategies","Kubernetes and cloud infrastructure (AWS, GCP)","Python or Rust"],"x-skills-preferred":[],"datePosted":"2026-04-18T16:00:17.377Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"High-performance, large-scale distributed systems, Implementing and deploying machine learning systems at scale, Load balancing, request routing, or traffic management systems, LLM inference optimization, batching, and caching strategies, Kubernetes and cloud infrastructure (AWS, GCP), Python or Rust","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":225000,"maxValue":325000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7d4c3fc5-2ed"},"title":"Senior Software Engineer, Inference","description":"<p>About the role:</p>\n<p>Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. 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However, some roles may require more time in our offices.</p>\n<p>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.</p>\n<p>How we&#39;re different:</p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p>The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>\n<p>Come work with us!</p>\n<p>Anthropic is a public benefit corporation headquartered in San Francisco. 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Responsibilities span data curation, modeling, training, inference serving, and product integration, covering both pretraining and post-training phases. You will collaborate closely with product teams to push model frontiers and deliver exceptional end-to-end user experiences.</p>\n<p>Key responsibilities include creating and driving engineering agendas to advance multimodal capabilities, improving data quality through annotation, filtering, augmentation, synthetic generation, captioning, and in-depth data studies, designing evaluation frameworks, metrics, benchmarks, evals, and reward models tailored to image/video/audio quality and coherence, implementing efficient algorithms for state-of-the-art model performance, and developing scalable data collection and processing pipelines for multimodal (primarily image/video-focused) datasets.</p>\n<p>The ideal candidate will have a track record in leading studies that significantly improve neural network capabilities and performance through better data or modeling, experience in data-driven experiment designs, systematic analysis, and iterative model debugging, experience developing or working with large-scale distributed machine learning systems, and ability to deliver optimal end-to-end user experiences.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_8a3caae4-044","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5051985007","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$180,000 - $440,000 USD","x-skills-required":["data curation","modeling","training","inference serving","product integration","large-scale distributed machine learning systems"],"x-skills-preferred":["SFT","RL","evals","human/synthetic data collection","agentic systems","Python","JAX/XLA","PyTorch","Rust/C++","Spark","Ray"],"datePosted":"2026-04-18T15:58:43.641Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto, CA; Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"data curation, modeling, training, inference serving, product integration, large-scale distributed machine learning systems, SFT, RL, evals, human/synthetic data collection, agentic systems, Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":440000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8871a994-591"},"title":"Machine Learning Engineer, Core Engineering","description":"<p>We&#39;re seeking a talented Machine Learning Engineer to join our Core Engineering team. As a Machine Learning Engineer at Pinterest, you will build cutting-edge technology using the latest advances in deep learning and machine learning to personalize Pinterest. You will partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces, while gaining knowledge of how ML works in different areas.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Build cutting-edge technology using the latest advances in deep learning and machine learning to personalize Pinterest</li>\n<li>Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas</li>\n<li>Use data-driven methods and leverage the unique properties of our data to improve candidate retrieval</li>\n<li>Work in a high-impact environment with quick experimentation and product launches</li>\n<li>Keep up with industry trends in recommendation systems</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)</li>\n<li>End-to-end hands-on experience with building data processing pipelines, large-scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)</li>\n<li>Degree in computer science, machine learning, statistics, or related field</li>\n</ul>\n<p>Nice to Have:</p>\n<ul>\n<li>M.S. or PhD in Machine Learning or related areas</li>\n<li>Publications at top ML conferences</li>\n<li>Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring</li>\n<li>Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration</li>\n<li>Expertise in scalable real-time systems that process stream data</li>\n<li>Passion for applied ML and the Pinterest product</li>\n</ul>\n<p>Relocation Statement:</p>\n<p>This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.</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_8871a994-591","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Pinterest","sameAs":"https://www.pinterest.com/","logo":"https://logos.yubhub.co/pinterest.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/pinterest/jobs/6121450","x-work-arrangement":"remote","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$138,905-$285,982 USD","x-skills-required":["machine learning","deep learning","data processing pipelines","large-scale machine learning systems","big data technologies","Hadoop","Spark","natural language processing","reinforcement learning","graph representation learning"],"x-skills-preferred":["Cursor","Copilot","Codex","LLM-powered productivity tools","scalable real-time systems","stream data"],"datePosted":"2026-04-18T15:57:30.186Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US; Remote, US"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, deep learning, data processing pipelines, large-scale machine learning systems, big data technologies, Hadoop, Spark, natural language processing, reinforcement learning, graph representation learning, Cursor, Copilot, Codex, LLM-powered productivity tools, scalable real-time systems, stream data","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":138905,"maxValue":285982,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_88c39a28-f4b"},"title":"Staff Software Engineer - AI SDK","description":"<p>We&#39;re looking for a Staff Software Engineer to join our AI SDK team. As a member of this team, you will be creating building blocks to support the expanding ecosystem of AI applications. Temporal provides durable execution, the systems foundation used for reliable AI at leaders such as OpenAI, Lovable, Replit, and many others.</p>\n<p>The AI SDK team is pushing to keep Temporal at the forefront of AI applications. Because the landscape is constantly changing, we engage heavily in prototyping to ensure that the abstractions we develop meet the needs of emerging applications. We also ensure that Temporal integrates well with leading AI frameworks and libraries.</p>\n<p>As a Staff Software Engineer, you will:</p>\n<ul>\n<li>Work as a Software Engineer.</li>\n<li>Design and implement Temporal AI SDK features supporting a broad variety of frameworks and libraries.</li>\n<li>Develop a deep understanding of AI application development techniques, including emerging approaches and architectures.</li>\n<li>Work with multiple programming languages, primarily Python and TypeScript.</li>\n<li>Make extensive use of AI coding tools, especially to ensure quality across a large number of integrations.</li>\n<li>Take end-to-end ownership of new features, working with other teams to deliver exceptional reliability and a great developer experience.</li>\n<li>Serve as a domain expert on AI design patterns, collaborating with field staff to provide best-practices and canonical examples.</li>\n<li>Work directly with our developer community to debug issues that need expert attention, and get feedback on Temporal SDK features and APIs.</li>\n<li>Write public technical documentation describing Temporal concepts and APIs.</li>\n<li>Go the extra mile to support a customer in need, on the rare occasion that AI SDK engineering expertise is needed.</li>\n<li>Travel to meet your coworkers for a week once or twice a year.</li>\n<li>Attend the occasional developer conference to talk about how great Temporal is (optional).</li>\n</ul>\n<p>You won&#39;t:</p>\n<ul>\n<li>Work as a Data Scientist, Data Analyst, Devops SWE, or SRE.</li>\n<li>Work in an office (unless you want to, but you&#39;d be by yourself). Temporal is a fully-remote company.</li>\n<li>Commit code that&#39;s poorly-tested or works &#39;most of the time&#39;. Temporal aspires to be &#39;Reliable as Gravity&#39;, and we expect our code to be the same.</li>\n<li>Work behind closed doors. The SDKs are open source,that means PRs and comments are open to the public, too.</li>\n<li>Sit in meetings all day. We mostly communicate in writing, and use meetings mainly for status updates and thorny issues that need input from the whole group.</li>\n<li>Wake up to pager alerts. We&#39;re extremely active in supporting our customers and the community, but we don&#39;t do 24/7 on-call.</li>\n</ul>\n<p>You&#39;ll bring:</p>\n<ul>\n<li>Experience and passion for harnessing generative AI, particularly for agents and coding.</li>\n<li>A deep understanding of how to use AI to increase quality, not only to increase quantity.</li>\n<li>A sense of taste in code and software development practice. Your approach should be opinionated and thoughtful, but not dogmatic.</li>\n<li>A track record of open source software contributions, including contributions to 3rd party libraries.</li>\n<li>Fluency in multiple programming languages, and an affinity for learning new ones.</li>\n<li>Deep experience with concurrent programming,you should know how to use mutexes, atomics, and other concurrency primitives safely.</li>\n<li>Experience designing APIs and writing documentation for publicly-available libraries or modules.</li>\n<li>Strong technical communication skills,written and verbal,in English.</li>\n<li>BS or MS in Computer Science (or a closely-related degree), or equivalent work experience writing production-grade software.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Familiarity with Temporal&#39;s programming model (e.g. you&#39;ve written an app on Temporal).</li>\n<li>Expedite building agents or other AI applications</li>\n<li>Background in machine learning, model training, data science, or machine learning systems.</li>\n<li>Experience contributing to the architecture and design of large-scale distributed systems.</li>\n<li>Graduate degree in Computer Science.</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_88c39a28-f4b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Temporal","sameAs":"https://github.com/temporalio","logo":"https://logos.yubhub.co/github.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/temporaltechnologies/jobs/4853421007","x-work-arrangement":"remote","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$224,000 - $302,400","x-skills-required":["Generative AI","Concurrent programming","API design","Documentation writing","Open source software contributions","Python","TypeScript","Machine learning","Data science"],"x-skills-preferred":["Temporal's programming model","Agent building","Machine learning systems","Distributed system design"],"datePosted":"2026-04-18T15:57:00.478Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"United States - Remote Opportunity"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Generative AI, Concurrent programming, API design, Documentation writing, Open source software contributions, Python, TypeScript, Machine learning, Data science, Temporal's programming model, Agent building, Machine learning systems, Distributed system design","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":224000,"maxValue":302400,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ac45e205-e7d"},"title":"Engineering Manager, Inference Routing and Performance","description":"<p><strong>About the role\\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what&#39;s already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\\n\\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn&#39;t have been shed.\\n\\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic&#39;s inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\\n\\nAs Anthropic moves from &quot;many independent inference replicas&quot; toward &quot;a single warehouse-scale computer running a coordinated program,&quot; Dystro is the coordination layer. This is a deeply technical team.\\n\\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\\n\\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\\n\\nThe EM for this team doesn&#39;t need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\\n\\nYou&#39;ll inherit a strong team of distributed-systems engineers, and you&#39;ll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\\n\\nThe job is holding both.\\n\\n## Representative work:\\nThings the Inference Routing EM actually spends time on:\\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they&#39;d be additive to a team that already goes deep\\n\\n## What you&#39;ll do:\\nDrive system-level performance\\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\\n- Build the team&#39;s habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\\n\\nDeliver reliably and operate cleanly\\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\\n- Run the team&#39;s operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You&#39;ll make sure commitments are realistic, dependencies are understood, and nobody is surprised\\n\\nBuild and grow the team\\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you&#39;ll help make that collaboration pattern productive\\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\\n\\n## You may be a good fit if you:\\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\\n- Are curious about machine learning systems. You don&#39;t need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\\n\\nStrong candidates may also have:\\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\\n- Led teams at supercomputing or hyperscaler infrastructure scale\\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\\n\\nThe annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\nAnnual Salary: $405,000-$485,000 USD</strong></p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ac45e205-e7d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5155391008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$405,000-$485,000 USD","x-skills-required":["engineering management","distributed systems","load balancing","scheduling","cache-coherent distributed state","high-performance networking","machine learning systems"],"x-skills-preferred":["LLM inference serving","cluster schedulers","load balancers","service meshes","coordination planes","heterogeneous accelerator fleets","GPU/TPU/Trainium","GPU/accelerator programming","ML framework internals","OS-level performance debugging"],"datePosted":"2026-04-18T15:56:48.587Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"engineering management, distributed systems, load balancing, scheduling, cache-coherent distributed state, high-performance networking, machine learning systems, LLM inference serving, cluster schedulers, load balancers, service meshes, coordination planes, heterogeneous accelerator fleets, GPU/TPU/Trainium, GPU/accelerator programming, ML framework internals, OS-level performance debugging","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":405000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_0b5a4347-f37"},"title":"Sr. Machine Learning Engineer, Monetization Engineering","description":"<p>About this role:</p>\n<p>We&#39;re looking for a Senior Machine Learning Engineer to join our Monetization team. As a key member of the team, you will be responsible for developing and executing a vision for the evolution of the machine learning technology stack within Ads.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Building cutting-edge technology using the latest advances in deep learning and machine learning to personalize Pinterest</li>\n<li>Partnering closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search)</li>\n<li>Using data-driven methods and leveraging the unique properties of our data to improve candidate retrieval</li>\n<li>Working in a high-impact environment with quick experimentation and product launches</li>\n<li>Keeping up with industry trends in recommendation systems</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>2+ years of industry experience applying machine learning methods</li>\n<li>Degree in computer science, statistics, or related field; or equivalent experience</li>\n<li>End-to-end hands-on experience with building data processing pipelines, large-scale machine learning systems, and big data technologies</li>\n<li>Practical knowledge of large-scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>M.S. or PhD in Machine Learning or related areas</li>\n<li>Publications at top ML conferences</li>\n<li>Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring</li>\n<li>Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration</li>\n<li>Expertise in scalable real-time systems that process stream data</li>\n<li>Passion for applied ML and the Pinterest product</li>\n<li>Background in computational advertising</li>\n</ul>\n<p>Relocation Statement:</p>\n<p>This position is not eligible for relocation assistance.</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_0b5a4347-f37","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Pinterest","sameAs":"https://www.pinterest.com/","logo":"https://logos.yubhub.co/pinterest.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/pinterest/jobs/6121551","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$189,721-$332,012 USD","x-skills-required":["Machine Learning","Deep Learning","Data Processing Pipelines","Large-Scale Machine Learning Systems","Big Data Technologies","Recommender Systems","Ads Ranking","Retrieval","Targeting","Marketplace Systems"],"x-skills-preferred":["M.S. or PhD in Machine Learning or related areas","Publications at top ML conferences","Experience using Cursor, Copilot, Codex, or similar AI coding assistants","Familiarity with LLM-powered productivity tools","Expertise in scalable real-time systems","Passion for applied ML and the Pinterest product","Background in computational advertising"],"datePosted":"2026-04-18T15:56:06.423Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Deep Learning, Data Processing Pipelines, Large-Scale Machine Learning Systems, Big Data Technologies, Recommender Systems, Ads Ranking, Retrieval, Targeting, Marketplace Systems, M.S. or PhD in Machine Learning or related areas, Publications at top ML conferences, Experience using Cursor, Copilot, Codex, or similar AI coding assistants, Familiarity with LLM-powered productivity tools, Expertise in scalable real-time systems, Passion for applied ML and the Pinterest product, Background in computational advertising","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":189721,"maxValue":332012,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_bf6a8614-973"},"title":"Events Manager, GenAI","description":"<p>We&#39;re looking for a talented Events Manager to join our Events and Experiences team supporting the GenAI Unit. This is a rare opportunity to plan and deliver internal events that help drive connection, collaboration and knowledge sharing across multiple locations, time zones and disciplines.</p>\n<p>Our internal events play a key role in helping to shape and amplify Google DeepMind&#39;s culture, enabling collaboration and celebrating key moments for the organisation at an important stage of our journey.</p>\n<p>As an Events Manager, you will be embedded in the GenAI Unit and will focus on events and experiences for this groundbreaking team whose mission is to build state of the art models and accelerate intelligent experiences across Google&#39;s platforms and products.</p>\n<p>You will partner with teams across Google DeepMind and Google to deliver a variety of events across the globe including Town Halls, Summits, hackathons, offsites and celebrations to drive collaboration, knowledge sharing and connection at scale.</p>\n<p>Key responsibilities:</p>\n<ul>\n<li>Own and strategically develop the full suite of GenAI Unit events programs, from large scale team events to intimate team gatherings.</li>\n</ul>\n<ul>\n<li>Respond nimbly to briefs from stakeholders to create event experiences in support of specific objectives, often with short lead times.</li>\n</ul>\n<ul>\n<li>Adapt and innovate, bringing fresh and innovative approaches in an ever-changing environment.</li>\n</ul>\n<ul>\n<li>Manage end to end event delivery and execution of large scale 2500+person events as well as small activations (for example: team summits, leadership meetings, launch celebrations).</li>\n</ul>\n<ul>\n<li>Domestic and international travel for global events programs.</li>\n</ul>\n<ul>\n<li>Create detailed project plans and timelines, tracking workstreams and deliverables, and flagging and mitigating risks.</li>\n</ul>\n<ul>\n<li>Create detailed briefs (documents and presentations) for executive stakeholders.</li>\n</ul>\n<ul>\n<li>Build budgets, tracking spend and processing contracts and purchase orders.</li>\n</ul>\n<ul>\n<li>Consult and partner with teams within the GenAI unit and across Google DeepMind (Events &amp; Experiences, Enterprise Engineering, Workplace, Comms, Marketing etc) coordinating cross functional working groups to deliver seamless experiences that meet the agreed objectives.</li>\n</ul>\n<ul>\n<li>Brief and collaborate with external agencies and internal Google vendor partners on venue, technical production and logistics requirements.</li>\n</ul>\n<ul>\n<li>Track and analyze agreed event metrics to assess success and to inform future event programs.</li>\n</ul>\n<ul>\n<li>Ensure all events are compliant with Google&#39;s health and safety, regulatory, and other governance policies, providing a secure and welcoming environment for all.</li>\n</ul>\n<p>To set you up for success as an Events Manager at Google DeepMind, we look for the following skills and experience:</p>\n<ul>\n<li>Exceptional attention to detail.</li>\n</ul>\n<ul>\n<li>Exceptional end to end project management skills and proven experience managing high complexity events through the full lifecycle, including planning, budgeting, execution, and retrospectives.</li>\n</ul>\n<ul>\n<li>Ability to throttle. Experience and passion for leading both large scale 2000+ person events as well as smaller scale activations with varying scopes and audiences.</li>\n</ul>\n<ul>\n<li>Comfortable navigating change and ambiguity in a very fast paced and demanding environment.</li>\n</ul>\n<ul>\n<li>Natural problem solving skills with a wildly creative, innovative, and curious approach to the work.</li>\n</ul>\n<p>Open to new ideas and learning opportunities, even when deadlines are nearing.</p>\n<ul>\n<li>Ability to simultaneously manage multiple events at different stages and meet all deadlines.</li>\n</ul>\n<ul>\n<li>Excellent relationship building skills. Values colleagues as partners and has a long term lens on cross-org relationships.</li>\n</ul>\n<ul>\n<li>Interest or experience in science and innovative technology, including in the field of artificial intelligence research or deployment of machine learning systems.</li>\n</ul>\n<ul>\n<li>Must work from the Mountain View office 3 days a week.</li>\n</ul>\n<ul>\n<li>Flexibility for both national and international travel, at times on short notice.</li>\n</ul>\n<ul>\n<li>Minimum 8 years of experience in events leadership at the scales and scopes mentioned.</li>\n</ul>\n<ul>\n<li>Ability and curiosity to use AI tools practically and effectively in your work, with a recognition and awareness of AI’s responsible use, risks, and limitations.</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_bf6a8614-973","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/7428956","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"The US base salary range for this full-time position is between $142,000 - $205,000 + bonus + equity + benefits.","x-skills-required":["Event management","Project management","Attention to detail","Problem-solving skills","Communication skills","Leadership skills","Experience in AI research or deployment of machine learning systems"],"x-skills-preferred":["AI tools","Machine learning","Data analysis","Event planning software","Budgeting and financial management"],"datePosted":"2026-04-18T15:54:09.825Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Event management, Project management, Attention to detail, Problem-solving skills, Communication skills, Leadership skills, Experience in AI research or deployment of machine learning systems, AI tools, Machine learning, Data analysis, Event planning software, Budgeting and financial management","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":142000,"maxValue":205000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_e394b0fa-2ba"},"title":"Staff Software Engineer, Inference","description":"<p><strong>About the role</strong></p>\n<p>Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>As a Staff Software Engineer on our Inference team, you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.</p>\n<p><strong>Strong candidates may also have experience with</strong></p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP)</li>\n<li>Python or Rust</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Representative projects across the org</strong></p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p><strong>Deadline to apply</strong></p>\n<p>None. Applications will be reviewed on a rolling basis.</p>\n<p><strong>Annual compensation range</strong></p>\n<p>The annual compensation range for this role is £325,000-£390,000 GBP.</p>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience</li>\n<li>Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience</li>\n<li>Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position</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>Why work with us?</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. 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We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. 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You will partner closely with different Gen AI counterparts to quickly bring the latest and greatest model capabilities from Google DeepMind&#39;s Gen AI unit into the product.</p>\n<p>Your core responsibilities will include:</p>\n<ul>\n<li>Define and execute the overall technical strategy for the Create area within Gemini App, aligning with the broader product outlook and driving daily usage.</li>\n</ul>\n<ul>\n<li>Lead and manage multiple engineering teams focused on features like content input and understanding, and content generation.</li>\n</ul>\n<ul>\n<li>Partner closely with Product Management, User Experience, and Data Science leads to define product roadmaps and deliver new LLM-forward capabilities.</li>\n</ul>\n<ul>\n<li>Drive the integration of GenAI model capabilities into Gemini App products.</li>\n</ul>\n<ul>\n<li>Foster a culture of innovation, collaboration, and rapid iteration within the engineering teams.</li>\n</ul>\n<ul>\n<li>Establish and track key performance indicators for the Create area, focusing on DAU for Gemini App.</li>\n</ul>\n<ul>\n<li>Manage cross-functional dependencies and collaborations with Gen AI, Workspace, Labs, and others as required to deliver great features.</li>\n</ul>\n<ul>\n<li>Ensure the scalability, reliability, and performance of Create-related features.</li>\n</ul>\n<p>Candidate Qualifications</p>\n<p>In order to set you up for success, we look for the following skills and experience:</p>\n<ul>\n<li>Bachelor&#39;s degree in Computer Science or Engineering, or equivalent practical experience.</li>\n</ul>\n<ul>\n<li>15 years of experience in software engineering, building and working with systems in the technology organization.</li>\n</ul>\n<ul>\n<li>7 years of experience managing teams of software engineers and managers of software engineers.</li>\n</ul>\n<p>In addition, the following would be an advantage:</p>\n<ul>\n<li>Master&#39;s or PhD degree in Computer Science, or a related technical field.</li>\n</ul>\n<ul>\n<li>Experience incubating and scaling new product initiatives from conception (0 to 1) to broad adoption in a fast-paced or startup environment.</li>\n</ul>\n<ul>\n<li>Expertise and direct experience in the application and deployment of large-scale AI/Machine Learning systems, particularly in generative AI for consumer products.</li>\n</ul>\n<p>Salary</p>\n<p>The US base salary range for this full-time position is between $307,000 - $427,000 + bonus + equity + benefits.</p>\n<p>Benefits</p>\n<p>At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. 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For more information on how we handle your data, please see our Applicant and Candidate Privacy Policy.</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_91ac9752-912","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/7821851","x-work-arrangement":"onsite","x-experience-level":"executive","x-job-type":"full-time","x-salary-range":"$307,000 - $427,000 + bonus + equity + benefits","x-skills-required":["Bachelor's degree in Computer Science or Engineering","15 years of experience in software engineering","7 years of experience managing teams of software engineers","Master's or PhD degree in Computer Science","Experience incubating and scaling new product 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Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.</strong></p>\n<p><strong>Strong candidates may also have experience with</strong></p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP)</li>\n<li>Python or Rust</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Representative projects across the org</strong></p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p><strong>Deadline to apply: None. Applications will be reviewed on a rolling basis.</strong></p>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</li>\n</ul>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</strong></p>\n<p><strong>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f95fe525-8fd","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5097742008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"£325,000 - £390,000GBP","x-skills-required":["performance optimization","distributed systems","large-scale service orchestration","intelligent request routing","LLM inference optimization","batching strategies","multi-accelerator deployments","Kubernetes","cloud infrastructure","Python","Rust"],"x-skills-preferred":["high-performance, large-scale distributed systems","implementing and deploying machine learning systems at scale","load balancing, request routing, or traffic management systems","caching strategies"],"datePosted":"2026-03-08T13:49:42.673Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"performance optimization, distributed systems, large-scale service orchestration, intelligent request routing, LLM inference optimization, batching strategies, multi-accelerator deployments, Kubernetes, cloud infrastructure, Python, Rust, high-performance, large-scale distributed systems, implementing and deploying machine learning systems at scale, load balancing, request routing, or traffic management systems, caching strategies","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":325000,"maxValue":390000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ca53b3f7-f72"},"title":"Staff / Senior Software Engineer, Inference","description":"<p><strong>About the role</strong></p>\n<p>Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry&#39;s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.</p>\n<p>The team has a dual mandate: <strong>maximizing compute efficiency</strong> to serve our explosive customer growth, while <strong>enabling breakthrough research</strong> by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.</p>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant software engineering experience, particularly with distributed systems</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Enjoy pair programming (we love to pair!)</li>\n<li>Want to learn more about machine learning systems and infrastructure</li>\n<li>Thrive in environments where technical excellence directly drives both business results and research breakthroughs</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>High-performance, large-scale distributed systems</li>\n<li>Implementing and deploying machine learning systems at scale</li>\n<li>Load balancing, request routing, or traffic management systems</li>\n<li>LLM inference optimization, batching, and caching strategies</li>\n<li>Kubernetes and cloud infrastructure (AWS, GCP, Azure)</li>\n<li>Python or Rust</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators</li>\n<li>Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads</li>\n<li>Building production-grade deployment pipelines for releasing new models to millions of users</li>\n<li>Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage</li>\n<li>Contributing to new inference features (e.g., structured sampling, prompt caching)</li>\n<li>Supporting inference for new model architectures</li>\n<li>Analyzing observability data to tune performance based on real-world production workloads</li>\n<li>Managing multi-region deployments and geographic routing for global customers</li>\n</ul>\n<p><strong>Deadline to apply:</strong></p>\n<p>None. Applications will be reviewed on a rolling basis.</p>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</strong></p>\n<p><strong>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p>The easiest way to understand our research directions is to read our recent research. This research co</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ca53b3f7-f72","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4951696008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$300,000 - $485,000 USD","x-skills-required":["distributed systems","machine learning systems","load balancing","request routing","traffic management","LLM inference optimization","Kubernetes","cloud infrastructure","Python","Rust"],"x-skills-preferred":["high-performance distributed systems","implementing and deploying machine learning systems at scale","structured sampling","prompt caching"],"datePosted":"2026-03-08T13:49:03.736Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, machine learning systems, load balancing, request routing, traffic management, LLM inference optimization, Kubernetes, cloud infrastructure, Python, Rust, high-performance distributed systems, implementing and deploying machine learning systems at scale, structured sampling, prompt caching","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":300000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_b7a4a1e1-a48"},"title":"Researcher, Alignment","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>Research</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></p>\n<p>The Alignment team at OpenAI is dedicated to ensuring that our AI systems are safe, trustworthy, and consistently aligned with human values, even as they scale in complexity and capability. Our work is at the cutting edge of AI research, focusing on developing methodologies that enable AI to robustly follow human intent across a wide range of scenarios, including those that are adversarial or high-stakes. We concentrate on the most pressing challenges, ensuring our work addresses areas where AI could have the most significant consequences. By focusing on risks that we can quantify and where our efforts can make a tangible difference, we aim to ensure that our models are ready for the complex, real-world environments in which they will be deployed.</p>\n<p>The two pillars of our approach are: (1) harnessing improved capabilities into alignment, making sure that our alignment techniques improve, rather than break, as capabilities grow, and (2) centering humans by developing mechanisms and interfaces that enable humans to both express their intent and to effectively supervise and control AIs, even in highly complex situations.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Research Engineer / Research Scientist on the Alignment team, you will be at the forefront of ensuring that our AI systems consistently follow human intent, even in complex and unpredictable scenarios. Your role will involve designing and implementing scalable solutions that ensure the alignment of AI as their capabilities grow and that integrate human oversight into AI decision-making.</p>\n<p>This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>In this role, you will:</strong></p>\n<p>We are seeking research engineers and research scientists to help design and implement experiments for alignment research. Responsibilities may include:</p>\n<ul>\n<li>Develop and evaluate alignment capabilities that are subjective, context-dependent, and hard to measure.</li>\n<li>Design evaluations to reliably measure risks and alignment with human intent and values.</li>\n<li>Build tools and evaluations to study and test model robustness in different situations.</li>\n<li>Design experiments to understand laws for how alignment scales as a function of compute, data, lengths of context and action, as well as resources of adversaries.</li>\n<li>Design and evaluate new Human-AI-interaction paradigms and scalable oversight methods that redefine how humans interact with, understand, and supervise our models.</li>\n<li>Train model to be calibrated on correctness and risk.</li>\n<li>Designing novel approaches for using AI in alignment research</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Are a team player – willing to do a variety of tasks that move the team forward.</li>\n<li>Have a PhD or equivalent experience in research in computer science, computational science, data science, cognitive science, or similar fields.</li>\n<li>Have strong engineering skills, particularly in designing and optimizing large-scale machine learning systems(e.g., PyTorch).</li>\n<li>Have a deep understanding of the science behind alignment algorithms and techniques.</li>\n<li>Can develop data visualization or data collection interfaces (e.g., TypeScript, Python).</li>\n<li>Enjoy fast-paced, collaborative, and cutting-edge research environments.</li>\n<li>Want to focus on developing AI models that are trustworthy, safe, and reliable, especially in high-stakes scenarios.</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 of our team members, partners, and users.</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_b7a4a1e1-a48","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/941bad28-7abe-43c7-b20a-2bc7e5b3c6e8","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$250K – $445K","x-skills-required":["Research in computer science, computational science, data science, cognitive science, or similar fields","Strong engineering skills, particularly in designing and optimizing large-scale machine learning systems(e.g., PyTorch)","Deep understanding of the science behind alignment algorithms and techniques","Data visualization or data collection interfaces (e.g., TypeScript, Python)","Fast-paced, collaborative, and cutting-edge research environments"],"x-skills-preferred":["PhD or equivalent experience","Team player – willing to do a variety of tasks that move the team forward","Enjoy developing AI models that are trustworthy, safe, and reliable, especially in high-stakes scenarios"],"datePosted":"2026-03-06T18:38:47.275Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Research in computer science, computational science, data science, cognitive science, or similar fields, Strong engineering skills, particularly in designing and optimizing large-scale machine learning systems(e.g., PyTorch), Deep understanding of the science behind alignment algorithms and techniques, Data visualization or data collection interfaces (e.g., TypeScript, Python), Fast-paced, collaborative, and cutting-edge research environments, PhD or equivalent experience, Team player – willing to do a variety of tasks that move the team forward, Enjoy developing AI models that are trustworthy, safe, and reliable, especially in high-stakes scenarios","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_75ad55ca-61b"},"title":"Research Engineer / Research Scientist - Foundations Retrieval IC","description":"<p><strong>Job Posting</strong></p>\n<p><strong>Research Engineer / Research Scientist - Foundations Retrieval IC</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>Research</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$445K – $555K • 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 Foundations Research team works on high-risk, high-reward ideas that could shape the next decade of AI. Our goal is to advance the science and data that enable our training and scaling efforts, with a particular focus on future frontier models. Pushing the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science.</p>\n<p><strong>About the Role</strong></p>\n<p>We’re looking for a researcher focused on our embedding retrieval efforts. You’ll work with a team of world-class research scientists and engineers developing foundational technology that enables models to retrieve and condition on the right information, at the right time. This includes designing new embedding training objectives, scalable vector store architectures, and dynamic indexing methods.</p>\n<p>This work will support retrieval across many OpenAI products and internal research efforts, with opportunities for scientific publication and deep technical impact.</p>\n<p>This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Tackle embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.</li>\n</ul>\n<ul>\n<li>Collaborate with a team of researchers and engineers building end-to-end infrastructure for training, evaluating, and integrating embeddings into frontier models.</li>\n</ul>\n<ul>\n<li>Drive innovation in dense, sparse, and hybrid representation techniques, metric learning, and learning-to-retrieve systems.</li>\n</ul>\n<ul>\n<li>Collaborate closely with Pretraining, Inference, and other Research teams to integrate retrieval throughout the model lifecycle</li>\n</ul>\n<ul>\n<li>Contribute to OpenAI’s long-term vision of AI systems with memory and knowledge access capabilities rooted in learned representations.</li>\n</ul>\n<p><strong>You Might Thrive in This Role If You Have</strong></p>\n<ul>\n<li>Proven experience leading high-performance teams of researchers or engineers in ML infrastructure or foundational research.</li>\n</ul>\n<ul>\n<li>Deep technical expertise in representation learning, embedding models, or vector retrieval systems.</li>\n</ul>\n<ul>\n<li>Familiarity with transformer-based LLMs and how embedding spaces can interact with language model objectives.</li>\n</ul>\n<ul>\n<li>Research experience in areas such as contrastive learning, supervised or unsupervised embedding learning, or metric learning.</li>\n</ul>\n<ul>\n<li>A track record of building or scaling large machine learning systems, particularly embedding pipelines in production or research contexts.</li>\n</ul>\n<ul>\n<li>A first-principles mindset for challenging assumptions about how retrieval and memory should work for large models.</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. 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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>We bring OpenAI&#39;s technology to the world through products like ChatGPT and the OpenAI API.</p>\n<p>We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth.</p>\n<p><strong>About the Role</strong></p>\n<p>We are looking for an experienced Research Engineer to work on retrieval &amp; search problems across our API and ChatGPT. As the AI landscape has evolved over the last few years, retrieval &amp; search have emerged as key use cases for our models, and we are investing in ensuring that we can offer these search-based product experiences for our users. You will be at the center of our retrieval &amp; search efforts as a company, and the progress you drive here will reach millions of end users.</p>\n<p>In this role, you will:</p>\n<ul>\n<li>Work on retrieval &amp; search algorithms and methodologies in close collaboration with our research team, including problems in such domains as document search, enterprise search, knowledge retrieval, and web-scale search.</li>\n</ul>\n<ul>\n<li>Deploy these search methodologies into production in both the API and ChatGPT to be used by millions of end users.</li>\n</ul>\n<ul>\n<li>Explore novel research topics in retrieval &amp; search that may inform our product strategy in the medium and long term.</li>\n</ul>\n<ul>\n<li>Partner with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world</li>\n</ul>\n<p>You might thrive in this role if you:</p>\n<ul>\n<li>Have extensive prior experience building and maintaining production machine learning systems.</li>\n</ul>\n<ul>\n<li>Have prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases</li>\n</ul>\n<ul>\n<li>Have prior experience building and iterating on internet-scale search systems</li>\n</ul>\n<ul>\n<li>Own problems end-to-end, and are willing to pick up whatever knowledge you&#39;re missing to get the job done</li>\n</ul>\n<ul>\n<li>Have the ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines</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_084ce7bc-ab4","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/7322d344-9325-4a92-8445-0a2c4e9272f8","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$293K – $585K • Offers Equity","x-skills-required":["extensive prior experience building and maintaining production machine learning systems","prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases","prior experience building and iterating on internet-scale search systems","ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines"],"x-skills-preferred":["prior experience with retrieval & search algorithms and methodologies","experience with deploying search methodologies into production","ability to partner with researchers, engineers, product managers, and designers"],"datePosted":"2026-03-06T18:27:34.351Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"extensive prior experience building and maintaining production machine learning systems, prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases, prior experience building and iterating on internet-scale search systems, ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines, prior experience with retrieval & search algorithms and methodologies, experience with deploying search methodologies into production, ability to partner with researchers, engineers, product managers, and designers","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":293000,"maxValue":585000,"unitText":"YEAR"}}}]}