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As a Research Scientist/Research Engineer, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.</li>\n<li>Design and experiment new approaches to preference optimization.</li>\n<li>Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.</li>\n<li>Publish research findings in top-tier AI conferences.</li>\n</ul>\n<p>Ideal Candidate:</p>\n<ul>\n<li>Ph.D. or Master&#39;s degree in Computer Science, Machine Learning, AI, or a related field.</li>\n<li>Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.</li>\n<li>Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.</li>\n<li>Excellent written and verbal communication skills</li>\n<li>Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals</li>\n<li>Previous experience in a customer-facing role.</li>\n</ul>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. 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You will be building and optimising the platform to enable our next generation LLM training, inference and data curation.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Building, profiling and optimising our training and inference framework.</li>\n<li>Collaborating with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.</li>\n<li>Researching and integrating state-of-the-art technologies to optimise our ML system.</li>\n</ul>\n<p>The ideal candidate will have:</p>\n<ul>\n<li>Passionate about system optimisation.</li>\n<li>Experience with multi-node LLM training and inference.</li>\n<li>Experience with developing large-scale distributed ML systems.</li>\n<li>Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.</li>\n<li>Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash attention, etc.</li>\n</ul>\n<p>Nice to haves include demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.</p>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. 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As a key member of our team, you will build out our next-gen Agent RL training platform, integrating cutting-edge research into our training stack. You will train state-of-the-art models, design solutions for complex multi-agent systems, and collaborate with our team to deploy use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models.</p>\n<p>The ideal candidate will have 5+ years of LLM training in a production environment, experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO, and publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years. A PhD or Masters in Computer Science or a related field is required.</p>\n<p>In addition to a competitive salary, you will receive equity-based compensation, comprehensive health, dental, and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. 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The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM&#39;s, as well as evaluation of data quality.</p>\n<p>At Scale, we&#39;re uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale&#39;s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build, profile and optimize our training and inference framework</li>\n<li>Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation</li>\n<li>Research and integrate state-of-the-art technologies to optimize our ML system</li>\n</ul>\n<p>Ideal Candidate:</p>\n<ul>\n<li>Strong excitement about system optimization</li>\n<li>Experience with multi-node LLM training and inference</li>\n<li>Experience with developing large-scale distributed ML systems</li>\n<li>Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.</li>\n<li>Strong written and verbal communication skills and the ability to operate in a cross functional team environment</li>\n</ul>\n<p>Nice to Have:</p>\n<ul>\n<li>Demonstrated expertise in post-training methods &amp;/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.</li>\n</ul>\n<p>Compensation Packages:</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>Please note that our policy requires a 90-day waiting period before reconsidering candidates for the same role. 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As an Agent MLRE, you will be working on applying our Agent RL Training + Building algorithms to real-life enterprise datasets across our clients + benchmarks. 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Your work will directly shape how our models understand and optimize for what humans actually want , enabling Claude to be more useful, more reliable, and better aligned with human values.</p>\n<p>This role focuses on pushing the frontier of reward modeling for large language models. You&#39;ll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking. You&#39;ll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety.</p>\n<p>We&#39;re looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You&#39;ll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources. Your work will directly advance the science of how we train AI systems to be both highly capable and safe.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead research on novel reward model architectures and training approaches for RLHF</li>\n<li>Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability</li>\n<li>Research techniques to detect, characterize, and mitigate reward hacking and specification gaming</li>\n<li>Design experiments to understand reward model generalization, robustness, and failure modes</li>\n<li>Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines</li>\n<li>Contribute to research publications, blog posts, and internal documentation</li>\n<li>Mentor other researchers and help build institutional knowledge around reward modeling</li>\n</ul>\n<p>You may be a good fit if you</p>\n<ul>\n<li>Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning</li>\n<li>Have experience training and evaluating reward models for large language models</li>\n<li>Are comfortable designing and running large-scale experiments with significant computational resources</li>\n<li>Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor</li>\n<li>Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences</li>\n<li>Care deeply about building AI systems that are both highly capable and safe</li>\n</ul>\n<p>Strong candidates may also</p>\n<ul>\n<li>Have published research on reward modeling, preference learning, or RLHF</li>\n<li>Have experience with LLM-as-judge approaches, including calibration and reliability challenges</li>\n<li>Have worked on reward hacking, specification gaming, or related robustness problems</li>\n<li>Have experience with constitutional AI, debate, or other scalable oversight approaches</li>\n<li>Have contributed to production ML systems at scale</li>\n<li>Have familiarity with interpretability techniques as applied to understanding reward model behavior</li>\n</ul>\n<p>The annual compensation range for this role is $350,000-$500,000 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_b1be4c11-417","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/5024835008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$500,000 USD","x-skills-required":["reward modeling","RLHF","LLM-based evaluation and grading","rubric-driven approaches","reward hacking","specification gaming","large-scale experiments","computational resources","research and engineering","collaborative research","complex ideas communication","AI systems development"],"x-skills-preferred":["published research","LLM-as-judge approaches","calibration and reliability challenges","constitutional AI","debate","scalable oversight approaches","production ML systems","interpretability techniques"],"datePosted":"2026-04-18T15:57:50.755Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote-Friendly (Travel Required) | San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"reward modeling, RLHF, LLM-based evaluation and grading, rubric-driven approaches, reward hacking, specification gaming, large-scale experiments, computational resources, research and engineering, collaborative research, complex ideas communication, AI systems development, published research, LLM-as-judge approaches, calibration and reliability challenges, constitutional AI, debate, scalable oversight approaches, production ML systems, interpretability techniques","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_769c0070-5b2"},"title":"Research Scientist, Agent Robustness","description":"<p>As a Research Scientist working on Agent Robustness, you will work on the fundamental challenges of building AI agents that are safe and aligned with humans.</p>\n<p>For example, you might:</p>\n<ul>\n<li>Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them;</li>\n<li>Design and build harnesses to test AI agents&#39; tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment;</li>\n<li>Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use;</li>\n<li>Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents.</li>\n</ul>\n<p>Ideally you&#39;d have:</p>\n<ul>\n<li>Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance;</li>\n<li>Practical experience conducting technical research collaboratively;</li>\n<li>Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches;</li>\n<li>A track record of published research in machine learning, particularly in generative AI;</li>\n<li>At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development;</li>\n<li>Strong written and verbal communication skills to operate in a cross-functional team.</li>\n</ul>\n<p>Nice to have:</p>\n<ul>\n<li>Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools;</li>\n<li>Experience with red-teaming, prompt injection, or adversarial testing of AI systems.</li>\n</ul>\n<p>Our research interviews are crafted to assess candidates&#39; skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organisational culture. We will not ask any LeetCode-style questions. If you&#39;re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn&#39;t perfectly align with every requirement.</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 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_769c0070-5b2","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/4675684005","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$216,000-$270,000 USD","x-skills-required":["Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance","Practical experience conducting technical research collaboratively","Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches","A track record of published research in machine learning, particularly in generative AI","At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development"],"x-skills-preferred":["Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools","Experience with red-teaming, prompt injection, or adversarial testing of AI systems"],"datePosted":"2026-04-18T15:57:29.447Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance, Practical experience conducting technical research collaboratively, Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches, A track record of published research in machine learning, particularly in generative AI, At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development, Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools, Experience with red-teaming, prompt injection, or adversarial testing of AI systems","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":216000,"maxValue":270000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_854e95b5-76b"},"title":"Sr. Director of Product, Research and Training Infrastructure","description":"<p>CoreWeave is seeking a visionary Sr. Director of Product, Research Training Infrastructure to lead the product strategy and engineering execution for the services that power the most ambitious AI research labs in the world.</p>\n<p>This executive leader will own the product strategy and engineering execution for the Research Training Stack, focusing on the specialized orchestration, evaluation, and iteration tools required for massive-scale pre-training and post-training.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Frontier Orchestration: Oversee the evolution of SUNK (Slurm on Kubernetes) to provide researchers with deterministic, bare-metal performance through a cloud-native interface.</li>\n</ul>\n<ul>\n<li>Holistic Training Services: Drive the development of next-generation orchestrators and automated training-based evaluation frameworks that ensure model quality throughout the lifecycle.</li>\n</ul>\n<ul>\n<li>Post-Training Excellence: Build the infrastructure required for sophisticated Reinforcement Learning (RL) and RLHF pipelines, enabling labs to refine foundation models with maximum efficiency.</li>\n</ul>\n<ul>\n<li>Customer Advocacy: Act as the primary technical partner for lead researchers at global AI labs, translating their &#39;future-state&#39; requirements into actionable product roadmaps.</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>Proven leadership experience in engineering leadership, with at least 5+ years managing large-scale infrastructure at a top-tier research lab or an AI-native cloud provider.</li>\n</ul>\n<ul>\n<li>Deep, hands-on knowledge of Slurm, Kubernetes, and the specific networking requirements (InfiniBand/RDMA) for distributed training clusters.</li>\n</ul>\n<ul>\n<li>Research mindset and understanding of the &#39;pain points&#39; of a research scientist.</li>\n</ul>\n<ul>\n<li>Scaling experience delivering mission-critical services on multi-thousand GPU clusters (H100/Blackwell/Rubin architectures).</li>\n</ul>\n<ul>\n<li>Strategic vision to define &#39;what&#39;s next&#39; in the AI stack, from automated RL loops to specialized sandbox environments.</li>\n</ul>\n<p>Why CoreWeave?</p>\n<p>In 2026, CoreWeave is the foundation of the largest infrastructure buildout in human history. We are building AI Factories, not just data centers.</p>\n<ul>\n<li>Silicon-Up Innovation: Work directly with the latest NVIDIA architectures.</li>\n</ul>\n<ul>\n<li>Impact: You will be the architect of the environment that enables the next new discovery.</li>\n</ul>\n<p>Velocity: We move at the speed of the researchers we support, bypassing legacy cloud bottlenecks to deliver raw power.</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_854e95b5-76b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"CoreWeave","sameAs":"https://www.coreweave.com","logo":"https://logos.yubhub.co/coreweave.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/coreweave/jobs/4665964006","x-work-arrangement":"hybrid","x-experience-level":"executive","x-job-type":"full-time","x-salary-range":"$233,000 to $341,000","x-skills-required":["Slurm","Kubernetes","InfiniBand/RDMA","Distributed training clusters","GPU clusters","H100/Blackwell/Rubin architectures","Reinforcement Learning (RL)","RLHF pipelines"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:50:11.130Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Livingston, NJ / New York, NY / Sunnyvale, CA / Bellevue, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Slurm, Kubernetes, InfiniBand/RDMA, Distributed training clusters, GPU clusters, H100/Blackwell/Rubin architectures, Reinforcement Learning (RL), RLHF pipelines","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":233000,"maxValue":341000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8549c317-12f"},"title":"Senior Research Scientist, Reward Models","description":"<p>As a Senior Research Scientist on our Reward Models team, you&#39;ll lead research efforts to improve how we specify and learn human preferences at scale.</p>\n<p>Your work will directly shape how our models understand and optimize for what humans actually want , enabling Claude to be more useful, more reliable, and better aligned with human values.</p>\n<p>This role focuses on pushing the frontier of reward modeling for large language models. You&#39;ll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking.</p>\n<p>You&#39;ll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety.</p>\n<p>We&#39;re looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You&#39;ll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources.</p>\n<p>Your work will directly advance the science of how we train AI systems to be both highly capable and safe.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead research on novel reward model architectures and training approaches for RLHF</li>\n</ul>\n<ul>\n<li>Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability</li>\n</ul>\n<ul>\n<li>Research techniques to detect, characterize, and mitigate reward hacking and specification gaming</li>\n</ul>\n<ul>\n<li>Design experiments to understand reward model generalization, robustness, and failure modes</li>\n</ul>\n<ul>\n<li>Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines</li>\n</ul>\n<ul>\n<li>Contribute to research publications, blog posts, and internal documentation</li>\n</ul>\n<ul>\n<li>Mentor other researchers and help build institutional knowledge around reward modeling</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning</li>\n</ul>\n<ul>\n<li>Have experience training and evaluating reward models for large language models</li>\n</ul>\n<ul>\n<li>Are comfortable designing and running large-scale experiments with significant computational resources</li>\n</ul>\n<ul>\n<li>Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor</li>\n</ul>\n<ul>\n<li>Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences</li>\n</ul>\n<ul>\n<li>Care deeply about building AI systems that are both highly capable and safe</li>\n</ul>\n<p>Strong candidates may also:</p>\n<ul>\n<li>Have published research on reward modeling, preference learning, or RLHF</li>\n</ul>\n<ul>\n<li>Have experience with LLM-as-judge approaches, including calibration and reliability challenges</li>\n</ul>\n<ul>\n<li>Have worked on reward hacking, specification gaming, or related robustness problems</li>\n</ul>\n<ul>\n<li>Have experience with constitutional AI, debate, or other scalable oversight approaches</li>\n</ul>\n<ul>\n<li>Have contributed to production ML systems at scale</li>\n</ul>\n<ul>\n<li>Have familiarity with interpretability techniques as applied to understanding reward model behavior</li>\n</ul>\n<p>The annual compensation range for this role is $350,000-$500,000 USD.</p>\n<p>Logistics:</p>\n<ul>\n<li>Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience</li>\n</ul>\n<ul>\n<li>Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience</li>\n</ul>\n<ul>\n<li>Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position</li>\n</ul>\n<p>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.</p>\n<p>How we&#39;re different:</p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p>The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>\n<p>Come work with us!</p>\n<p>Anthropic is a public benefit corporation headquartered in San Francisco. 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_8549c317-12f","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/5024835008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$500,000 USD","x-skills-required":["reward modeling","RLHF","large language models","novel architectures","training methodologies","evaluation and grading","rubric-based methods","reward hacking","specification gaming","generalization","robustness","failure modes","computational resources","scientific rigor","communication skills","interpretability techniques"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:47:13.514Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote-Friendly (Travel Required) | San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"reward modeling, RLHF, large language models, novel architectures, training methodologies, evaluation and grading, rubric-based methods, reward hacking, specification gaming, generalization, robustness, failure modes, computational resources, scientific rigor, communication skills, interpretability techniques","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_157be224-49f"},"title":"Machine Learning Systems Engineer, RL Engineering","description":"<p>About the role:</p>\n<p>You want to build the cutting-edge systems that train AI models like Claude. You&#39;re excited to work at the frontier of machine learning, implementing and improving advanced techniques to create ever more capable, reliable and steerable AI.</p>\n<p>As an ML Systems Engineer on our Reinforcement Learning Engineering team, you&#39;ll be responsible for the critical algorithms and infrastructure that our researchers depend on to train models. Your work will directly enable breakthroughs in AI capabilities and safety.</p>\n<p>You&#39;ll focus obsessively on improving the performance, robustness, and usability of these systems so our research can progress as quickly as possible.</p>\n<p>Our finetuning researchers train our production Claude models, and internal research models, using RLHF and other related methods. Your job will be to build, maintain, and improve the algorithms and systems that these researchers use to train models.</p>\n<p>You&#39;ll be responsible for improving the speed, reliability, and ease-of-use of these systems.</p>\n<p>Strong candidates may also have experience with:</p>\n<p>High performance, large scale distributed systems Large scale LLM training Python Implementing LLM finetuning algorithms, such as RLHF</p>\n<p>Representative projects:</p>\n<p>Profiling our reinforcement learning pipeline to find opportunities for improvement Building a system that regularly launches training jobs in a test environment so that we can quickly detect problems in the training pipeline Making changes to our finetuning systems so they work on new model architectures Building instrumentation to detect and eliminate Python GIL contention in our training code Diagnosing why training runs have started slowing down after some number of steps, and fixing it Implementing a stable, fast version of a new training algorithm proposed by a researcher</p>\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 $500,000-$850,000 USD.</p>\n<p>Logistics:</p>\n<p>Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position 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. 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.</p>\n<p>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>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>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.</p>\n<p>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.</p>\n<p>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. 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>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_157be224-49f","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/4952051008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$500,000-$850,000 USD","x-skills-required":["High performance, large scale distributed systems","Large scale LLM training","Python","Implementing LLM finetuning algorithms, such as RLHF"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:43:06.423Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"High performance, large scale distributed systems, Large scale LLM training, Python, Implementing LLM finetuning algorithms, such as RLHF","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_2ab01f55-75b"},"title":"Research Scientist / Engineer, Honesty","description":"<p><strong>About the role:</strong></p>\n<p>As a Research Scientist/Engineer focused on honesty within the Finetuning Alignment team, you&#39;ll spearhead the development of techniques to minimise hallucinations and enhance truthfulness in language models. Your work will focus on creating robust systems that are accurate and reflect their true levels of confidence across all domains, and that work to avoid being deceptive or misleading.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Design and implement novel data curation pipelines to identify, verify, and filter training data for accuracy given the model’s knowledge</li>\n<li>Develop specialised classifiers to detect potential hallucinations or miscalibrated claims made by the model</li>\n<li>Create and maintain comprehensive honesty benchmarks and evaluation frameworks</li>\n<li>Implement techniques to ground model outputs in verified information, such as search and retrieval-augmented generation (RAG) systems</li>\n<li>Design and deploy human feedback collection specifically for identifying and correcting miscalibrated responses</li>\n<li>Design and implement prompting pipelines to generate data that improves model accuracy and honesty</li>\n<li>Develop and test novel RL environments that reward truthful outputs and penalize fabricated claims</li>\n<li>Create tools to help human evaluators efficiently assess model outputs for accuracy</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have an MS/PhD in Computer Science, ML, or related field</li>\n<li>Possess strong programming skills in Python</li>\n<li>Have industry experience with language model finetuning and classifier training</li>\n<li>Show proficiency in experimental design and statistical analysis for measuring improvements in calibration and accuracy</li>\n<li>Care about AI safety and the accuracy and honesty of both current and future AI systems</li>\n<li>Have experience in data science or the creation and curation of datasets for finetuning LLMs</li>\n<li>An understanding of various metrics of uncertainty, calibration, and truthfulness in model outputs</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Published work on hallucination prevention, factual grounding, or knowledge integration in language models</li>\n<li>Experience with fact-grounding techniques</li>\n<li>Background in developing confidence estimation or calibration methods for ML models</li>\n<li>A track record of creating and maintaining factual knowledge bases</li>\n<li>Familiarity with RLHF specifically applied to improving model truthfulness</li>\n<li>Worked with crowd-sourcing platforms and human feedback collection systems</li>\n<li>Experience developing evaluations of model accuracy or hallucinations</li>\n</ul>\n<p><strong>Join us in our mission to ensure advanced AI systems behave reliably and ethically while staying aligned with human values.</strong></p>\n<p><strong>The annual compensation range for this role is listed below.</strong></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>$350,000 - $500,000USD</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. 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 team to push the boundaries of what is possible with AI.</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_2ab01f55-75b","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/4532887008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $500,000USD","x-skills-required":["Python","Language model finetuning","Classifier training","Experimental design","Statistical analysis","Data science","Dataset curation"],"x-skills-preferred":["Fact-grounding techniques","Confidence estimation","Calibration methods","RLHF","Crowd-sourcing platforms","Human feedback collection systems"],"datePosted":"2026-03-08T13:46:41.247Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York City, NY; San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Language model finetuning, Classifier training, Experimental design, Statistical analysis, Data science, Dataset curation, Fact-grounding techniques, Confidence estimation, Calibration methods, RLHF, Crowd-sourcing platforms, Human feedback collection systems","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_34f86990-9e0"},"title":"Machine Learning Systems Engineer, RL Engineering","description":"<p><strong>About the role:</strong></p>\n<p>You want to build the cutting-edge systems that train AI models like Claude. You&#39;re excited to work at the frontier of machine learning, implementing and improving advanced techniques to create ever more capable, reliable and steerable AI. As an ML Systems Engineer on our Reinforcement Learning Engineering team, you&#39;ll be responsible for the critical algorithms and infrastructure that our researchers depend on to train models. Your work will directly enable breakthroughs in AI capabilities and safety. You&#39;ll focus obsessively on improving the performance, robustness, and usability of these systems so our research can progress as quickly as possible. You&#39;re energized by the challenge of supporting and empowering our research team in the mission to build beneficial AI systems.</p>\n<p>Our finetuning researchers train our production Claude models, and internal research models, using RLHF and other related methods. Your job will be to build, maintain, and improve the algorithms and systems that these researchers use to train models. You’ll be responsible for improving the speed, reliability, and ease-of-use of these systems.</p>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have 4+ years of software engineering experience</li>\n<li>Like working on systems and tools that make other people more productive</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 research</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>Large scale LLM training</li>\n<li>Python</li>\n<li>Implementing LLM finetuning algorithms, such as RLHF</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Profiling our reinforcement learning pipeline to find opportunities for improvement</li>\n<li>Building a system that regularly launches training jobs in a test environment so that we can quickly detect problems in the training pipeline</li>\n<li>Making changes to our finetuning systems so they work on new model architectures</li>\n<li>Building instrumentation to detect and eliminate Python GIL contention in our training code</li>\n<li>Diagnosing why training runs have started slowing down after some number of steps, and fixing it</li>\n<li>Implementing a stable, fast version of a new training algorithm proposed by a researcher</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 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 — 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</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_34f86990-9e0","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/4952051008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$500,000 - $850,000USD","x-skills-required":["High performance, large scale distributed systems","Large scale LLM training","Python","Implementing LLM finetuning algorithms, such as RLHF"],"x-skills-preferred":["High performance, large scale distributed systems","Large scale LLM training","Python","Implementing LLM finetuning algorithms, such as RLHF"],"datePosted":"2026-03-08T13:45:59.930Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"High performance, large scale distributed systems, Large scale LLM training, Python, Implementing LLM finetuning algorithms, such as RLHF, High performance, large scale distributed systems, Large scale LLM training, Python, Implementing LLM finetuning algorithms, such as RLHF","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_01a10ada-f52"},"title":"Technical Lead, Safety Research","description":"<p><strong>Technical Lead, Safety Research</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>$460K – $555K</li>\n</ul>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.</p>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p><strong>About the Team</strong></p>\n<p>The 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><strong>About the Role</strong></p>\n<p>As a tech lead, you will be responsible for developing our strategy in new directions to address potential harms from misalignment or significant mistakes. This will in practice include:</p>\n<ul>\n<li>Setting north star goals and milestones for new research directions, and developing challenging evaluations to track progress.</li>\n</ul>\n<ul>\n<li>Personally driving or leading research in new exploratory directions to demonstrate feasibility and scalability of the approaches.</li>\n</ul>\n<ul>\n<li>Working horizontally across safety research and related teams to ensure different technical approaches work together to achieve strong safety results.</li>\n</ul>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Set the research directions and strategies to make our AI systems safer, more aligned and more robust.</li>\n</ul>\n<ul>\n<li>Coordinate and collaborate with cross-functional teams, including the rest of the research organization, T&amp;S, policy and related alignment teams, to ensure that our AI meets the highest safety standards.</li>\n</ul>\n<ul>\n<li>Actively evaluate and understand the safety of our models and systems, identifying areas of risk and proposing mitigation strategies.</li>\n</ul>\n<ul>\n<li>Conduct state-of-the-art research on AI safety topics such as RLHF, adversarial training, robustness, and more.</li>\n</ul>\n<ul>\n<li>Implement new methods in OpenAI’s core model training and launch safety improvements in OpenAI’s products.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Are excited about OpenAI’s mission of building safe, universally beneficial AGI and are aligned with OpenAI’s charter</li>\n</ul>\n<ul>\n<li>Demonstrate a passion for AI safety and making cutting-edge AI models safer for real-world use.</li>\n</ul>\n<ul>\n<li>Bring 4+ years of experience in the field of AI safety, especially in areas like RLHF, adversarial training, robustness, fairness &amp; biases.</li>\n</ul>\n<ul>\n<li>Hold a Ph.D. or other degree in computer science, machine learning, or a related field.</li>\n</ul>\n<ul>\n<li>Possess experience in safety work for AI model deployment</li>\n</ul>\n<ul>\n<li>Have an in-depth understanding of deep learning research and/or strong engineering skills.</li>\n</ul>\n<ul>\n<li>Are a team player who enjoys collaborative work environments.</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.</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_01a10ada-f52","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/273b4c99-273e-4a70-aff9-19c0d959dcef","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$460K – $555K","x-skills-required":["AI safety","RLHF","adversarial training","robustness","fairness & biases","deep learning research","engineering skills"],"x-skills-preferred":["team player","collaborative work environments"],"datePosted":"2026-03-06T18:40:40.069Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"AI safety, RLHF, adversarial training, robustness, fairness & biases, deep learning research, engineering skills, team player, collaborative work environments","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":460000,"maxValue":555000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_28cb565e-69a"},"title":"Researcher, Health AI","description":"<p><strong>Researcher, Health AI</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>About the Team</strong></p>\n<p>The Safety Systems team is dedicated to ensuring the safety, robustness, and reliability of AI models towards their deployment in the real world.</p>\n<p>OpenAI’s charter calls on us to ensure the benefits of AI are distributed widely. Our Health AI team is focused on enabling universal access to high-quality medical information. We work at the intersection of AI safety research and healthcare applications, aiming to create trustworthy AI models that can assist medical professionals and improve patient outcomes.</p>\n<p><strong>About the Role</strong></p>\n<p>We’re seeking strong researchers who are passionate about advancing AI safety and improving global health outcomes. As a Research Scientist, you will contribute to the development of safe and effective AI models for healthcare applications. You will implement practical and general methods to improve the behavior, knowledge, and reasoning of our models in these settings. This will require research into safety and alignment techniques that we aim to generalize towards safe and beneficial AGI.</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<ul>\n<li>Design and apply practical and scalable methods to improve safety and reliability of our models, including RLHF, automated red teaming, scalable oversight, etc.</li>\n</ul>\n<ul>\n<li>Evaluate methods using health-related data, ensuring models provide accurate, reliable, and trustworthy information.</li>\n</ul>\n<ul>\n<li>Build reusable libraries for applying general alignment techniques to our models.</li>\n</ul>\n<ul>\n<li>Proactively understand the safety of our models and systems, identifying areas of risk.</li>\n</ul>\n<ul>\n<li>Work with cross-team stakeholders to integrate methods in core model training and launch safety improvements in OpenAI’s products.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Are excited about OpenAI’s mission of ensuring AGI is universally beneficial and are aligned with OpenAI’s charter.</li>\n</ul>\n<ul>\n<li>Demonstrate passion for AI safety and improving global health outcomes.</li>\n</ul>\n<ul>\n<li>Have 4+ years of experience with deep learning research and LLMs, especially practical alignment topics such as RLHF, automated red teaming, scalable oversight, etc.</li>\n</ul>\n<ul>\n<li>Hold a Ph.D. or other degree in computer science, AI, machine learning, or a related field.</li>\n</ul>\n<ul>\n<li>Stay goal-oriented instead of method-oriented, and are not afraid of unglamorous but high-value work when needed.</li>\n</ul>\n<ul>\n<li>Possess experience making practical model improvements for AI model deployment.</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>Are a team player who enjoys collaborative work environments.</li>\n</ul>\n<ul>\n<li>Bonus: possess experience in health-related AI research or deployments.</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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This requires a breadth of new ML research to address the growing set of safety challenges as AI becomes more powerful and used in more settings.  Key focus areas include how to enforce nuanced safety policies without trading off helpfulness and capabilities, how to make the model robust to adversaries, how to address privacy and security risks, and how to make the model trustworthy in safety-critical domains.</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.</p>\n<p><strong><strong>About the Role</strong></strong></p>\n<p>OpenAI is seeking a senior researcher with passion for AI safety and experience in safety research. Your role will set directions for research to enable and empower safe AGI and work on research projects to make our AI systems safer, more aligned and more robust to adversarial or malicious use cases. You will play a critical role in shaping how a safe AI system should look like in the future at OpenAI, making a significant impact on our mission to build and deploy safe AGI.</p>\n<p><strong><strong>In this role, you will:</strong></strong></p>\n<ul>\n<li>Conduct state-of-the-art research on AI safety topics such as RLHF, adversarial training, robustness, and more.</li>\n</ul>\n<ul>\n<li>Implement new methods in OpenAI’s core model training and launch safety improvements in OpenAI’s products.</li>\n</ul>\n<ul>\n<li>Set the research directions and strategies to make our AI systems safer, more aligned and more robust.</li>\n</ul>\n<ul>\n<li>Coordinate and collaborate with cross-functional teams, including T&amp;S, legal, policy and other research teams, to ensure that our products meet the highest safety standards.</li>\n</ul>\n<ul>\n<li>Actively evaluate and understand the safety of our models and systems, identifying areas of risk and proposing mitigation strategies.</li>\n</ul>\n<p><strong><strong>You might thrive in this role if you:</strong></strong></p>\n<ul>\n<li>Are excited about OpenAI’s mission of building safe, universally beneficial AGI and are aligned with OpenAI’s charter</li>\n</ul>\n<ul>\n<li>Demonstrate a passion for AI safety and making cutting-edge AI models safer for real-world use.</li>\n</ul>\n<ul>\n<li>Bring 4+ years of experience in the field of AI safety, especially in areas like RLHF, adversarial training, robustness, fairness &amp; biases.</li>\n</ul>\n<ul>\n<li>Hold a Ph.D. or other degree in computer science, machine learning, or a related field.</li>\n</ul>\n<ul>\n<li>Possess experience in safety work for AI model deployment</li>\n</ul>\n<ul>\n<li>Have an in-depth understanding of deep learning research and/or strong engineering skills.</li>\n</ul>\n<ul>\n<li>Are a team player who enjoys collaborative work environments.</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. 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This requires a breadth of new ML research in the areas of human-AI collaboration, reasoning, robustness, and scalable oversight to keep pace with model capabilities.  We invest heavily in developing novel model and system-level methods of identifying and mitigating AI misuse and misalignment.</p>\n<p>Our goal is to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely.</p>\n<p><strong>About the Role</strong></p>\n<p>OpenAI is seeking a senior researcher with a passion for AI safety and experience in safety research. Your role will set directions for research to maintain effective oversight of safe AGI and work on research projects to identify and mitigate misuse and misalignment in our AI systems. You will play a critical role in defining how a safe AI system should look in the future at OpenAI, making a significant impact on our mission to build and deploy safe AGI.</p>\n<p>In this role, you will:</p>\n<ul>\n<li>Develop and refine AI monitor models to detect and mitigate known and emerging patterns of misuse and misalignment.</li>\n</ul>\n<ul>\n<li>Set research directions and strategies to make our AI systems safer, more aligned, and more robust.</li>\n</ul>\n<ul>\n<li>Evaluate and design effective red-teaming pipelines to examine the end-to-end robustness of our safety systems, and identify areas for future improvement.</li>\n</ul>\n<ul>\n<li>Conduct research to improve models’ ability to reason about questions of human values, and apply these improved models to practical safety challenges.</li>\n</ul>\n<ul>\n<li>Coordinate and collaborate with cross-functional teams, including T&amp;S, legal, policy and other research teams, to ensure that our products meet the highest safety standards.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Are excited about OpenAI’s mission of building safe, universally beneficial AGI and are aligned with OpenAI’s charter</li>\n</ul>\n<ul>\n<li>Show enthusiasm for AI safety and dedication to enhancing the safety of cutting-edge AI models for real-world use.</li>\n</ul>\n<ul>\n<li>Bring 4+ years of experience in the field of AI safety, especially in areas like RLHF, human-AI collaboration, fairness &amp; biases.</li>\n</ul>\n<ul>\n<li>Hold a Ph.D. or other degree in computer science, machine learning, or a related field.</li>\n</ul>\n<ul>\n<li>Thrive in environments involving large-scale AI systems.</li>\n</ul>\n<ul>\n<li>Possess 4+ years of research engineering experience and proficiency in Python or similar languages.</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. 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