{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/ml-research"},"x-facet":{"type":"skill","slug":"ml-research","display":"Ml Research","count":19},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9a42f26c-511"},"title":"Evals Engineer, Applied AI","description":"<p>We are seeking a technically rigorous and driven AI Research Engineer to join our Enterprise Evaluations team. This high-impact role is critical to our mission of delivering the industry&#39;s leading GenAI Evaluation Suite.</p>\n<p>As a hands-on contributor to the core systems that ensure the safety, reliability, and continuous improvement of LLM-powered workflows and agents for the enterprise, you will partner with Scale&#39;s Operations team and enterprise customers to translate ambiguity into structured evaluation data. This involves guiding the creation and maintenance of gold-standard human-rated datasets and expert rubrics that anchor AI evaluation systems.</p>\n<p>Your responsibilities will also include analysing feedback and collected data to identify patterns, refine evaluation frameworks, and establish iterative improvement loops that enhance the quality and relevance of human-curated assessments. You will design, research, and develop LLM-as-a-Judge autorater frameworks and AI-assisted evaluation systems, including creating models that critique, grade, and explain agent outputs.</p>\n<p>To succeed in this role, you will need a strong foundational knowledge of large language models, a passion for tackling complex evaluation challenges, and the ability to thrive in a dynamic, fast-paced research environment. You should be able to think outside the box, stay current with the latest literature in AI evaluation, and be passionate about integrating novel research ideas into our workflows to build best-in-class evaluation systems.</p>\n<p>In addition to your technical expertise, you will need excellent communication and collaboration skills, as you will work closely with cross-functional teams to drive project success.</p>\n<p>If you are a motivated and detail-oriented individual with a passion for AI research and evaluation, we encourage you to apply for this exciting opportunity.</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_9a42f26c-511","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale AI","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4629589005","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$216,000-$270,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","Large Language Models","Generative AI","Machine Learning","Applied Research","Evaluation Infrastructure"],"x-skills-preferred":["Advanced degree in Computer Science, Machine Learning, or a related quantitative field","Published research in leading ML or AI conferences","Experience designing, building, or deploying LLM-as-a-Judge frameworks or other automated evaluation systems","Experience collaborating with operations or external teams to define high-quality human annotator guidelines","Expertise in ML research engineering, stochastic systems, observability, or LLM-powered applications for model evaluation and analysis"],"datePosted":"2026-04-18T16:01:26.736Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, Large Language Models, Generative AI, Machine Learning, Applied Research, Evaluation Infrastructure, Advanced degree in Computer Science, Machine Learning, or a related quantitative field, Published research in leading ML or AI conferences, Experience designing, building, or deploying LLM-as-a-Judge frameworks or other automated evaluation systems, Experience collaborating with operations or external teams to define high-quality human annotator guidelines, Expertise in ML research engineering, stochastic systems, observability, or LLM-powered applications for model evaluation and analysis","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_460d00aa-b48"},"title":"Senior / Staff+ Software Engineer, Voice Platform","description":"<p>About the role</p>\n<p>We&#39;re building the infrastructure that lets people talk to Claude,real-time, bidirectional voice conversations that feel natural, responsive, and safe. This is foundational work for how millions of people will interact with AI.</p>\n<p>The Voice Platform team designs and operates the serving systems, streaming pipelines, and APIs that bring Anthropic&#39;s audio models from research into production across Claude.ai, our mobile apps, and the Anthropic API. You&#39;ll work at the intersection of real-time media, low-latency inference, and distributed systems,building infrastructure where every millisecond of latency is felt by the user.</p>\n<p>We partner closely with the Audio research team, who train the speech understanding and generation models, and with product teams shipping voice experiences to users. Your job is to make those models fast, reliable, and delightful to talk to at scale.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Design and build the real-time streaming infrastructure that powers voice conversations with Claude,ingesting microphone audio, orchestrating model inference, and streaming synthesized speech back with minimal latency</li>\n</ul>\n<ul>\n<li>Build low-latency serving systems for speech models, optimizing time-to-first-audio and end-to-end conversational responsiveness</li>\n</ul>\n<ul>\n<li>Develop the public and internal APIs that expose voice capabilities to Claude.ai, mobile clients, and third-party developers</li>\n</ul>\n<ul>\n<li>Own the audio transport layer,codecs, jitter buffers, adaptive bitrate, packet loss recovery,so conversations stay smooth across unreliable networks</li>\n</ul>\n<ul>\n<li>Build observability and quality-measurement systems for voice: latency distributions, audio quality metrics, interruption handling, and turn-taking accuracy</li>\n</ul>\n<ul>\n<li>Partner with Audio research to move new model architectures from experiment to production, and feed real-world performance data back into research</li>\n</ul>\n<ul>\n<li>Collaborate with mobile and product engineering on client-side audio capture, playback, and the end-to-end user experience</li>\n</ul>\n<p>You may be a good fit if you</p>\n<ul>\n<li>Have 6+ years of experience building distributed systems, real-time infrastructure, or platform services at scale</li>\n</ul>\n<ul>\n<li>Have shipped production systems where latency is measured in tens of milliseconds and users notice when you miss</li>\n</ul>\n<ul>\n<li>Are comfortable working across the stack,from transport protocols and serving infrastructure up to the APIs product teams build on</li>\n</ul>\n<ul>\n<li>Are results-oriented, with a bias toward flexibility and impact</li>\n</ul>\n<ul>\n<li>Pick up slack, even if it goes outside your job description</li>\n</ul>\n<ul>\n<li>Enjoy pair 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As a member of our Safeguards team, you will be responsible for designing and overseeing the execution of capability evaluations to assess the cyber-relevant capabilities of new models. You will also create comprehensive cyber threat models, including attack vectors, exploit chains, precursor identification, and weaponization techniques.</p>\n<p>This is a unique opportunity to shape how frontier AI models handle dual-use cybersecurity knowledge,balancing the tremendous potential of AI to advance legitimate security research and defensive capabilities while preventing misuse by malicious actors.</p>\n<p>In this role, you will lead and grow a team of technical specialists focused on cyber threat modeling and evaluation frameworks. You will serve as the primary domain expert on cyber harms, advising cross-functional teams on threat landscapes and mitigation strategies.</p>\n<p>You will collaborate closely with internal and external threat modeling experts to develop training data for safety systems, and with ML engineers to train these systems, optimizing for both robustness against adversarial attacks and low false-positive rates for legitimate security researchers.</p>\n<p>You will also analyze safety system performance in traffic, identifying gaps and proposing improvements. You will conduct regular reviews of existing policies and enforcement systems to identify and address gaps and ambiguities related to cybersecurity risks.</p>\n<p>You will develop rigorous stress-testing of safeguards against evolving cyber threats and product surfaces. You will partner with Research, Product, Policy, Security Team, and Frontier Red Team to ensure cybersecurity safety is embedded throughout the model development lifecycle.</p>\n<p>You will translate cybersecurity domain knowledge into actionable safety requirements and clearly articulated policies. You will contribute to external communications, including model cards, blog posts, and policy documents related to cybersecurity safety.</p>\n<p>You will monitor emerging technologies and threat landscapes for their potential to contribute to new risks and mitigation strategies, and strategically address these.</p>\n<p>You will mentor and develop team members, fostering a culture of technical excellence and responsible AI development.</p>\n<p>To be successful in this role, you will need to have:</p>\n<ul>\n<li>An M.S. or PhD in Computer Science, Cybersecurity, or a related technical field, OR equivalent professional experience in offensive or defensive cybersecurity</li>\n<li>5+ years of hands-on experience in cybersecurity, with deep expertise in areas such as vulnerability research, exploit development, network security, malware analysis, or penetration testing</li>\n<li>2+ years of experience managing technical teams or leading complex technical projects with multiple stakeholders</li>\n<li>Experience in scientific computing and data analysis, with proficiency in programming (Python preferred)</li>\n<li>Deep expertise in modern cybersecurity, including both offensive techniques (vulnerability research, exploit development, penetration testing, malware analysis) and defensive measures (detection, monitoring, incident response)</li>\n<li>Demonstrated ability to create threat models and translate technical cyber risks into policy frameworks</li>\n<li>Familiarity with responsible disclosure practices, vulnerability coordination, and cybersecurity frameworks (e.g., MITRE ATT&amp;CK, NIST Cybersecurity Framework, CWE/CVE systems)</li>\n<li>Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders</li>\n<li>Experience developing policies or guidelines at scale, balancing safety concerns with enabling legitimate use cases</li>\n<li>A passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies</li>\n<li>Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve</li>\n<li>Track record of translating specialized technical knowledge into actionable safety policies or enforcement guidelines</li>\n</ul>\n<p>Preferred qualifications include:</p>\n<ul>\n<li>Background in AI/ML systems, particularly experience with large language models</li>\n<li>Experience developing ML-based security systems or adversarial ML research</li>\n<li>Experience working with defense, intelligence, or security organizations (e.g., NSA, CISA, national labs, security contractors)</li>\n<li>Published security research, disclosed vulnerabilities, or participated in bug bounty programs</li>\n<li>Understanding of Trust &amp; 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What makes that possible isn&#39;t just talent: it&#39;s the coordination, systems, and programs that let researchers spend their time on the science rather than the overhead around it.</p>\n<p>This role sits at the intersection of research operations, technical program management, and product strategy. You&#39;ll work directly with research scientists and research engineers, doing a mix of tasks including running research partnerships, managing complex internal programs, and helping run the team’s day-to-day operations.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Build and manage custom expert contractor networks, sourcing domain specialists for eval and training data work that requires expertise beyond standard channels</li>\n<li>Run research partnerships with external partners, from scoping through delivery</li>\n<li>Provide end-to-end TPM support for major research pushes,coordinating across teams, tracking dependencies, and keeping stakeholders aligned</li>\n<li>Ensure that our research progress is complemented by products that enable scientists to make maximal use of model capabilities.</li>\n<li>Support recruiting efforts.</li>\n<li>Coordinate external communications for the team, including supporting blog posts and preparing public-facing materials</li>\n<li>Partner with product teams to contribute to science product strategy, product design, and novel product integrations where research and product intersect</li>\n<li>Own team logistics including onboarding coordination, team events, and operational programs that improve team efficiency</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have experience in research operations, technical program management, or a related role in a fast-moving technical environment</li>\n<li>Can context-switch fluidly between operational work (logistics, tracking, coordination) and higher-order work (strategy, partnerships, product thinking)</li>\n<li>Have a technical background, with experience in software development, machine learning, or biology R&amp;D.</li>\n<li>Are comfortable working directly with research scientists and engineers,you ask good questions, you don&#39;t need things explained twice, and you know when to escalate vs. when to handle it yourself</li>\n<li>Have a track record of building systems and processes from scratch rather than inheriting them</li>\n<li>Bring strong written communication skills and can represent the team accurately in external-facing materials</li>\n<li>Have managed contractors or external partners before, including scoping work, tracking delivery, and ensuring quality</li>\n<li>Are results-oriented, with a bias toward flexibility and impact</li>\n<li>Thrive in ambiguous, fast-moving environments where priorities shift and no two weeks look the same</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Direct experience sourcing and managing expert contractor networks, particularly in technical or scientific domains</li>\n<li>Familiarity with ML research workflows,training runs, evaluations, data pipelines,and what makes them succeed or stall</li>\n<li>Experience contributing to product development or product strategy, not just operations</li>\n</ul>\n<p><strong>Logistics</strong></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 research operations role 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. 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You will contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Collaborating with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine</li>\n<li>Optimizing for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators</li>\n<li>Building and maintaining instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations</li>\n<li>Developing and enhancing scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads</li>\n<li>Supporting reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning</li>\n<li>Integrating with federated, distributed inference infrastructure – orchestrate across nodes, balance load, handle communication overhead</li>\n<li>Collaborating cross-functionally: with platform engineers, cloud infrastructure, and security/compliance teams</li>\n<li>Documenting and sharing learnings, contributing to internal best practices and open-source efforts when possible</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>BS/MS/PhD in Computer Science, or a related field</li>\n<li>Strong software engineering background (3+ years or equivalent) in performance-critical systems</li>\n<li>Solid understanding of ML inference internals: attention, MLPs, recurrent modules, quantization, sparse operations, etc.</li>\n<li>Hands-on experience with CUDA, GPU programming, and key libraries (cuBLAS, cuDNN, NCCL, etc.)</li>\n<li>Comfortable designing and operating distributed systems, including RPC frameworks, queuing, RPC batching, sharding, memory partitioning</li>\n<li>Demonstrated ability to uncover and solve performance bottlenecks across layers (kernel, memory, networking, scheduler)</li>\n<li>Experience building instrumentation, tracing, and profiling tools for ML models</li>\n<li>Ability to work closely with ML researchers, translate novel model ideas into production systems</li>\n<li>Ownership mindset and eagerness to dive deep into complex system challenges</li>\n<li>Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f2196e99-854","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8202670002","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$142,200-$204,600 USD","x-skills-required":["software engineering","performance-critical systems","ML inference internals","CUDA","GPU programming","distributed systems","RPC frameworks","queuing","RPC batching","sharding","memory partitioning","instrumentation","tracing","profiling tools","ML researchers","complex system challenges"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:54:17.777Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, performance-critical systems, ML inference internals, CUDA, GPU programming, distributed systems, RPC frameworks, queuing, RPC batching, sharding, memory partitioning, instrumentation, tracing, profiling tools, ML researchers, complex system challenges","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":142200,"maxValue":204600,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4fde2d89-11c"},"title":"Research Engineer, Economic Research","description":"<p>As a Research Engineer on the Economic Research team, you will design, build, and maintain critical infrastructure that powers Anthropic&#39;s research on AI&#39;s economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis.\\n\\nThe Economic Research team at Anthropic studies the economic implications of AI on individual, firm, and economy-wide outcomes. We build scalable systems to monitor AI usage patterns and directly measure the impact of AI adoption on real-world outcomes. We publish research and data that is clear-eyed about the economic effects of AI to help policymakers, businesses, and the public understand and navigate the transition to powerful AI.\\n\\nIn this role, you will work closely with teams across Anthropic,including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy,to build scalable and robust data systems that support high-leverage, high-impact research. Strong candidates will have a track record building data processing pipelines, architecting &amp; implementing high-quality internal infrastructure, working in a fast-paced startup environment, navigating ambiguity, and demonstrating an eagerness to develop their own research &amp; technical skills.\\n\\nResponsibilities:\\n\\n<em> Build and maintain data pipelines that process large scale Claude usage logs into canonical, reusable datasets while maintaining user privacy.\\n</em> Expand privacy-preserving tools to enable new analytic functionality to support research needs.\\n<em> Design and implement novel data systems leveraging language models (e.g., CLIO) where traditional software engineering patterns don&#39;t yet exist.\\n</em> Develop and maintain data pipelines that are interoperable across data sources (including ingesting external data) and are designed to support economic analysis.\\n<em> Contribute to the strategic development of the economic research data foundations roadmap\\n</em> Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure\\n<em> Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions\\n</em> Create documentation and best practices that enable self-serve data access for researchers while maintaining security and governance standards.\\n<em> Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission\\n\\nYou might be a good fit if you have:\\n\\n</em> Experience working with Research Scientists and Economists on ambiguous AI and economic projects\\n<em> Experience with building and maintaining data infrastructure, large datasets, and internal tools in production environments.\\n</em> Experience with cloud infrastructure platforms such as AWS or GCP.\\n<em> Take pride in writing clean, well-documented code in Python that others can build upon\\n</em> Are comfortable making technical decisions with incomplete information while maintaining high engineering standards\\n<em> Are comfortable getting up-to-speed quickly on unfamiliar codebases, and can work well with other engineers with different backgrounds across the organization\\n</em> Have a track record of using technical infrastructure to interface effectively with machine learning models\\n<em> Have experience deriving insights from imperfect data streams\\n</em> Have experience building systems and products on top of LLMs\\n<em> Have experience incubating and maturing tooling platforms used by a wide variety of stakeholders\\n</em> A passion for Anthropic&#39;s mission of building helpful, honest, and harmless AI and understanding its economic implications.\\n<em> A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description.\\n</em> Strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners who may have varying levels of technical expertise.\\n\\nStrong candidates may have:\\n\\n<em> Background in econometrics, statistics, or quantitative social science research\\n</em> Experience building data infrastructure and data foundations for research\\n<em> Familiarity with large language models, AI systems, or ML research workflows\\n</em> Prior work on projects related to labor economics, technology adoption, or economic measurement\\n\\nSome Examples of Our Recent Work\\n\\n<em> Anthropic Economic Index Report: Economic Primitives\\n</em> Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption\\n<em> Estimating AI productivity gains from Claude conversations\\n</em> The Anthropic Economic Index\\n\\nDeadline to apply: None. Applications are reviewed on a rolling basis\\n\\nThe annual compensation range for this role is listed below.\\n\\nFor sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\n\\nAnnual Salary: $300,000-$405,000 USD\\n\\nLogistics\\n\\nMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\\nRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience\\nMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\\nLocation-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\\nVisa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\\n\\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\\n\\nYour safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.\\n\\nHow we&#39;re different\\n\\nWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on small\\n</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_4fde2d89-11c","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/5071132008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$300,000-$405,000 USD","x-skills-required":["Python","Cloud infrastructure platforms (AWS or GCP)","Data infrastructure","Large datasets","Internal tools","Machine learning models","Econometrics","Statistics","Quantitative social science research","Large language models","AI systems","ML research workflows"],"x-skills-preferred":["Full-stack mindset","Strong communication skills","Ambiguity tolerance","Problem-solving skills","Collaboration skills"],"datePosted":"2026-04-18T15:52:18.267Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Cloud infrastructure platforms (AWS or GCP), Data infrastructure, Large datasets, Internal tools, Machine learning models, Econometrics, Statistics, Quantitative social science research, Large language models, AI systems, ML research workflows, Full-stack mindset, Strong communication skills, Ambiguity tolerance, Problem-solving skills, Collaboration skills","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":300000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d951cdda-dc5"},"title":"Research Operations, Discovery","description":"<p><strong>About the Role</strong></p>\n<p>We&#39;re seeking a Science Research Operations team member to build and own the operational infrastructure that keeps our research organisation running at full speed.</p>\n<p>Our science teams are working on some of the hardest and most consequential problems in AI,training large-scale models, running complex experiments, and building novel products at the frontier. What makes that possible isn&#39;t just talent: it&#39;s the coordination, systems, and programs that let researchers spend their time on the science rather than the overhead around it.</p>\n<p>This role sits at the intersection of research operations, technical program management, and product strategy. You&#39;ll work directly with research scientists and research engineers, doing a mix of tasks including running research partnerships, managing complex internal programs, and helping run the team’s day-to-day operations.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Build and manage custom expert contractor networks, sourcing domain specialists for eval and training data work that requires expertise beyond standard channels</li>\n<li>Run research partnerships with external partners, from scoping through delivery</li>\n<li>Provide end-to-end TPM support for major research pushes,coordinating across teams, tracking dependencies, and keeping stakeholders aligned</li>\n<li>Ensure that our research progress is complemented by products that enable scientists to make maximal use of model capabilities.</li>\n<li>Support recruiting efforts.</li>\n<li>Coordinate external communications for the team, including supporting blog posts and preparing public-facing materials</li>\n<li>Partner with product teams to contribute to science product strategy, product design, and novel product integrations where research and product intersect</li>\n<li>Own team logistics including onboarding coordination, team events, and operational programs that improve team efficiency</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have experience in research operations, technical program management, or a related role in a fast-moving technical environment</li>\n<li>Can context-switch fluidly between operational work (logistics, tracking, coordination) and higher-order work (strategy, partnerships, product thinking)</li>\n<li>Have a technical background, with experience in software development, machine learning, or biology R&amp;D.</li>\n<li>Are comfortable working directly with research scientists and engineers,you ask good questions, you don&#39;t need things explained twice, and you know when to escalate vs. when to handle it yourself</li>\n<li>Have a track record of building systems and processes from scratch rather than inheriting them</li>\n<li>Bring strong written communication skills and can represent the team accurately in external-facing materials</li>\n<li>Have managed contractors or external partners before, including scoping work, tracking delivery, and ensuring quality</li>\n<li>Are results-oriented, with a bias toward flexibility and impact</li>\n<li>Thrive in ambiguous, fast-moving environments where priorities shift and no two weeks look the same</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Direct experience sourcing and managing expert contractor networks, particularly in technical or scientific domains</li>\n<li>Familiarity with ML research workflows,training runs, evaluations, data pipelines,and what makes them succeed or stall</li>\n<li>Experience contributing to product development or product strategy, not just operations</li>\n</ul>\n<p><strong>Logistics</strong></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 research operations role 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. 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Your primary responsibility will be to design and build infrastructure that enables researchers to rapidly iterate on reward signals. This includes tools for rubric development, human feedback data analysis, and reward robustness evaluation. You will also develop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologies. Additionally, you will create tooling that allows researchers to easily compare different reward methodologies and understand their effects. You will collaborate with researchers to translate science requirements into platform capabilities and optimize existing systems for performance, reliability, and ease of use.</p>\n<p>You will have the opportunity to contribute directly to research projects yourself and have a direct impact on our ability to scale reward development across domains. You will work closely with researchers and translate ambiguous requirements into well-scoped engineering projects.</p>\n<p>To be successful in this role, you should have prior research experience and be excited to work closely with researchers. You should have strong Python skills and experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms. You should be comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing tooling.</p>\n<p>Strong candidates may also have experience with ML research, building internal tooling and platforms for ML researchers, data quality assessment and pipeline optimization, experiment tracking, evaluation frameworks, or MLOps tooling. They may also have experience with large-scale data processing, Kubernetes, distributed systems, or cloud infrastructure, and familiarity with reinforcement learning or fine-tuning workflows.</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_64176983-af0","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/5024831008","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$350,000-$500,000 USD","x-skills-required":["Python","ML workflows","data pipelines","infrastructure/tooling/platforms","rubric development","human feedback data analysis","reward robustness evaluation","automated quality assessment","reward hacks","pathologies","experiment tracking","evaluation frameworks","MLOps tooling"],"x-skills-preferred":["ML research","building internal tooling and platforms for ML researchers","data quality assessment and pipeline optimization","Kubernetes","distributed systems","cloud infrastructure","reinforcement learning","fine-tuning workflows"],"datePosted":"2026-04-18T15:42:43.065Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, ML workflows, data pipelines, infrastructure/tooling/platforms, rubric development, human feedback data analysis, reward robustness evaluation, automated quality assessment, reward hacks, pathologies, experiment tracking, evaluation frameworks, MLOps tooling, ML research, building internal tooling and platforms for ML researchers, data quality assessment and pipeline optimization, Kubernetes, distributed systems, cloud infrastructure, reinforcement learning, fine-tuning workflows","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_0119fcf6-354"},"title":"Technical Program Manager, Frontier AI Research","description":"<p><strong>Job Title</strong></p>\n<p>Technical Program Manager, Frontier AI Research</p>\n<p><strong>About Us</strong></p>\n<p>Google DeepMind is a technology company that develops artificial intelligence (AI) and machine learning (ML) systems.</p>\n<p><strong>The Role</strong></p>\n<p>As a Technical Program Manager in Frontier Research, you will navigate immense technical complexity and manage critical cross-functional dependencies at the leading edge of AI. You will apply deep technical expertise to perform engineering due diligence, influence high-level architecture, and accelerate the delivery of research.</p>\n<p><strong>Key Responsibilities</strong></p>\n<ul>\n<li>Program Strategy and Execution: Translate complex research and engineering efforts into execution plans and measurable goals, and actively drive delivery through sprints or equivalent methodologies.</li>\n<li>Technical Due Diligence: Utilize advanced TPgM tools and processes, evaluate engineering scope, establish technical roadmaps, and influence technical direction.</li>\n<li>Cross-Functional Leadership: Build and guide effective cross functional partnerships across diverse disciplines, including modeling, infrastructure, and evaluation, to ensure alignment on shared objectives.</li>\n<li>Operational Excellence: Build and optimize scalable operational frameworks, establishing robust mechanisms to monitor program performance and anticipate technical risks while strategically reducing your personal operational load to allow it to scale.</li>\n<li>Continuous Innovation: Proactively identify and implement opportunities to enhance research velocity and engineering excellence through optimized tools and methodologies.</li>\n<li>Stakeholder Management: Distill complex technical updates and research progress into clear, actionable insights for diverse audiences and executive leadership.</li>\n<li>Resource Optimization: Drive strategic planning for mission-critical resources, including compute management and allocation, to respond to a rapidly changing competitive landscape.</li>\n</ul>\n<p><strong>About You</strong></p>\n<ul>\n<li>Minimum Qualifications:</li>\n<li>Bachelor’s degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.</li>\n<li>5+ years of experience in Technical Program Management or a related role within an advanced technical or research environment.</li>\n<li>Proven experience partnering directly with research or engineering leads to deliver on high-impact, technically complex projects.</li>\n<li>Comprehensive technical knowledge and understanding of program management methodologies adapted for research-centric environments.</li>\n<li>Preferred Qualifications:</li>\n<li>Master’s or PhD degree in a technical field, particularly related to AI/machine learning.</li>\n<li>Direct experience in AI/ML research or development</li>\n<li>Strong analytical and problem-solving skills, with a demonstrated ability to understand complex technical systems and identify opportunities for optimization.</li>\n<li>Passion for the future of AI and the ability to thrive in a dynamic, fast-paced environment where technology meets real-world impact.</li>\n</ul>\n<p><strong>Salary</strong></p>\n<p>The US base salary range for this full-time position is between $163,000 - $237,000 + bonus + equity + benefits.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_0119fcf6-354","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/7556601","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$163,000 - $237,000 + bonus + equity + benefits","x-skills-required":["Technical Program Management","AI","Machine Learning","Program Management Methodologies","Research-Centric Environments","Cross-Functional Leadership","Operational Excellence","Continuous Innovation","Stakeholder Management","Resource Optimization"],"x-skills-preferred":["Master’s or PhD degree in a technical field","Direct experience in AI/ML research or development","Strong analytical and problem-solving skills","Passion for the future of AI"],"datePosted":"2026-04-18T15:40:52.617Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Technical Program Management, AI, Machine Learning, Program Management Methodologies, Research-Centric Environments, Cross-Functional Leadership, Operational Excellence, Continuous Innovation, Stakeholder Management, Resource Optimization, Master’s or PhD degree in a technical field, Direct experience in AI/ML research or development, Strong analytical and problem-solving skills, Passion for the future of AI","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":163000,"maxValue":237000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ef01837a-5e3"},"title":"Anthropic Fellows Program — AI Security","description":"<p><strong>About the Role</strong></p>\n<p>The Anthropic Fellows Program is a 4-month, full-time research opportunity for individuals to work on empirical AI research and engineering projects. As an AI Security Fellow, you will be part of a team that focuses on reducing catastrophic risks from advanced AI systems.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Conduct empirical AI research and engineering projects aligned with Anthropic&#39;s research priorities</li>\n<li>Collaborate with mentors and peers to achieve project goals</li>\n<li>Present research findings and results to the team and wider community</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Fluency in Python programming</li>\n<li>Strong technical background in computer science, mathematics, or physics</li>\n<li>Ability to implement ideas quickly and communicate clearly</li>\n</ul>\n<p><strong>Nice to Have</strong></p>\n<ul>\n<li>Experience with pentesting, vulnerability research, or other offensive security work</li>\n<li>Experience with empirical ML research projects</li>\n<li>Experience with deep learning frameworks and experiment management</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>To participate in the Fellows program, you must have work authorization in the UK and be located in the UK during the program</li>\n<li>Workspace locations: London and Berkeley</li>\n<li>Visa sponsorship: Not currently available</li>\n</ul>\n<p><strong>Application Process</strong></p>\n<p>Applications and interviews are managed by Constellation, our official recruiting partner for this program. Clicking &#39;Apply here&#39; will redirect you to Constellation&#39;s application portal.</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_ef01837a-5e3","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5030244008","x-work-arrangement":"onsite","x-experience-level":"entry|mid|senior|staff|executive","x-job-type":"full-time","x-salary-range":"$3,850 USD / £2,310 / $4,300 CAD per week","x-skills-required":["Python","Computer Science","Mathematics","Physics"],"x-skills-preferred":["Pentesting","Vulnerability Research","Offensive Security Work","Empirical ML Research Projects","Deep Learning Frameworks","Experiment Management"],"datePosted":"2026-04-18T15:38:42.812Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Computer Science, Mathematics, Physics, Pentesting, Vulnerability Research, Offensive Security Work, Empirical ML Research Projects, Deep Learning Frameworks, Experiment Management","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":2310,"maxValue":4300,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_38f09377-ea6"},"title":"Anthropic AI Safety Fellow","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</p>\n<p><strong>Apply using this link. We’re accepting applications on a rolling basis for cohorts starting in July 2026 and beyond. Applications for the May 2026 cohort are now closed.</strong></p>\n<p><strong>Anthropic Fellows Program Overview</strong></p>\n<p>The Anthropic Fellows Program is designed to accelerate AI safety research and foster research talent. We provide funding and mentorship to promising technical talent - regardless of previous experience - to research the frontier of AI safety for four months.</p>\n<p>Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In our previous cohorts, over 80% of fellows produced papers (more below).</p>\n<p>We run multiple cohorts of Fellows each year. This application is for cohorts starting in July 2026 and beyond.</p>\n<p><strong>What to Expect</strong></p>\n<ul>\n<li>Direct mentorship from Anthropic researchers</li>\n<li>Access to a shared workspace (in either Berkeley, California or London, UK)</li>\n<li>Connection to the broader AI safety research community</li>\n<li>Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD &amp; access to benefits (benefits vary by country)</li>\n<li>Funding for compute (~$15k/month) and other research expenses</li>\n</ul>\n<p><strong>Mentors, Research Areas, &amp; Past Projects</strong></p>\n<p>Fellows will undergo a project selection &amp; mentor matching process. Potential mentors amongst others include:</p>\n<ul>\n<li>Jan Leike</li>\n<li>Sam Bowman</li>\n<li>Sara Price</li>\n<li>Alex Tamkin</li>\n<li>Nina Panickssery</li>\n<li>Trenton Bricken</li>\n<li>Logan Graham</li>\n<li>Jascha Sohl-Dickstein</li>\n<li>Nicholas Carlini</li>\n<li>Joe Benton</li>\n<li>Collin Burns</li>\n<li>Fabien Roger</li>\n<li>Samuel Marks</li>\n<li>Kyle Fish</li>\n<li>Ethan Perez</li>\n</ul>\n<p>Our mentors will lead projects in select AI safety research areas, such as:</p>\n<ul>\n<li>Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.</li>\n<li>Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.</li>\n<li>Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.</li>\n<li>Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures.</li>\n<li>AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations.</li>\n</ul>\n<p>On our Alignment Science and Frontier Red Team blogs, you can read about past projects, including:</p>\n<ul>\n<li>AI agents find $4.6M in blockchain smart contract exploits: Winnie Xiao and Cole Killian, mentored by Nicholas Carlini and Alwin Peng</li>\n<li>Subliminal Learning: Language Models Transmit Behavioral Traits via Hidden Signals in Data: Alex Cloud and Minh Le, et al., mentors including Samuel Marks and Owain Evans</li>\n<li>Open-source circuits: Michael Hanna and Mateusz Piotrowski with mentorship from Emmanuel Ameisen and Jack Lindsey</li>\n</ul>\n<p>For a full list of representative projects for each area, please see these blog posts: Introducing the Anthropic Fellows Program for AI Safety Research, Recommendations for Technical AI Safety Research Directions.</p>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Are motivated by reducing catastrophic risks from advanced AI systems</li>\n<li>Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic</li>\n</ul>\n<p><strong>Please note: We do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit here at Anthropic. In previous cohorts, over 40% of fellows received a full-time offer, and we’ve supported many more to go on to do great work on safety at other organisations.</strong></p>\n<ul>\n<li>Have a strong technical background in computer science, mathematics, physics, cybersecurity, or related fields</li>\n<li>Thrive in fast-paced, collaborative environments</li>\n<li>Can implement ideas quickly and communicate clearly</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Experience with empirical ML research projects</li>\n<li>Experience working with Large Language Models</li>\n<li>Experience in one of the research areas mentioned above</li>\n<li>Experience with deep learning frameworks and experiment management</li>\n<li>Track record of open-source contributions</li>\n</ul>\n<p><strong>Candidates must be:</strong></p>\n<ul>\n<li>Fluent in Python programming</li>\n<li>Available to work full-time on the Fellows program for 4 months</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. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.</strong></p>\n<p><strong>Interview process</strong></p>\n<p>The interview process will include an initial application &amp; references check, technical assessments &amp; interviews, and a research discussion.</p>\n<p><strong>Compensation</strong></p>\n<p>The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation</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_38f09377-ea6","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/5023394008","x-work-arrangement":"remote","x-experience-level":"entry","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","Machine Learning","Deep Learning","Large Language Models","Empirical ML research projects","Deep learning frameworks","Experiment management"],"x-skills-preferred":["Open-source contributions","Track record of open-source contributions"],"datePosted":"2026-03-08T13:58:47.316Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Machine Learning, Deep Learning, Large Language Models, Empirical ML research projects, Deep learning frameworks, Experiment management, Open-source contributions, Track record of open-source contributions"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5fba9d7d-674"},"title":"AI Security Fellow","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</p>\n<p><strong>AI Security at Anthropic</strong></p>\n<p>We believe we are at an inflection point for AI&#39;s impact on cybersecurity. Models are now useful for cybersecurity tasks in practice: for example, Claude can now outperform human teams in some cybersecurity competitions and help us discover vulnerabilities in our own code.</p>\n<p>We are looking for researchers and engineers to help us accelerate defensive use of AI to secure code and infrastructure.</p>\n<p><strong>Anthropic Fellows Program Overview</strong></p>\n<p>The Anthropic Fellows Program is designed to accelerate AI security and safety research, and foster research talent. We provide funding and mentorship to promising technical talent - regardless of previous experience - to research the frontier of AI security and safety for four months.</p>\n<p>Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In our previous cohorts, over 80% of fellows produced papers (more below).</p>\n<p>We run multiple cohorts of Fellows each year. This application is for cohorts starting in July 2026 and beyond.</p>\n<p><strong>What to Expect</strong></p>\n<ul>\n<li>Direct mentorship from Anthropic researchers</li>\n<li>Access to a shared workspace (in either Berkeley, California or London, UK)</li>\n<li>Connection to the broader AI safety research community</li>\n<li>Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD &amp; access to benefits (benefits vary by country)</li>\n<li>Funding for compute (~$15k/month) and other research expenses</li>\n</ul>\n<p><strong>Mentors, Research Areas, &amp; Past Projects</strong></p>\n<p>Fellows will undergo a project selection &amp; mentor matching process. Potential mentors include:</p>\n<ul>\n<li>Nicholas Carlini</li>\n<li>Keri Warr</li>\n<li>Evyatar Ben Asher</li>\n<li>Keane Lucas</li>\n<li>Newton Cheng</li>\n</ul>\n<p>On our Alignment Science and Frontier Red Team blogs, you can read about some past Fellows projects, including:</p>\n<ul>\n<li>AI agents find $4.6M in blockchain smart contract exploits: Winnie Xiao and Cole Killian, mentored by Nicholas Carlini and Alwin Peng</li>\n<li>Strengthening Red Teams: A Modular Scaffold for Control Evaluations: Chloe Loughridge et al., mentored by Jon Kutasov and Joe Benton</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Are motivated by reducing catastrophic risks from advanced AI systems</li>\n<li>Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic</li>\n</ul>\n<p><strong>Please note:</strong></p>\n<p>We do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit here at Anthropic. In previous cohorts, over 40% of fellows received a full-time offer, and we’ve supported many more to go on to do great work on safety at other organisations.</p>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Contributed to open-source projects in LLM- or security-adjacent repositories</li>\n<li>Demonstrated success in bringing clarity and ownership to ambiguous technical problems</li>\n<li>Experience with pentesting, vulnerability research, or other offensive security</li>\n<li>A history demonstrating desire to do the &#39;dirty work&#39; that results in high-quality outputs</li>\n<li>Reported CVEs, or been awarded for bug bounty vulnerabilities</li>\n<li>Experience with empirical ML research projects</li>\n<li>Experience with deep learning frameworks and experiment management</li>\n</ul>\n<p><strong>Candidates must be:</strong></p>\n<ul>\n<li>Fluent in Python programming</li>\n<li>Available to work full-time on the Fellows program for 4 months</li>\n</ul>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong></p>\n<p>Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p><strong>Interview process</strong></p>\n<p>The interview process will include an initial application &amp; references check, technical assessments &amp; interviews, and a research discussion.</p>\n<p><strong>Compensation</strong></p>\n<p>The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week, for 4 months (with possible extension).</p>\n<p><strong>Logistics</strong></p>\n<p>Logistics Requirements: To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program.</p>\n<p>Workspace Locations: We have designated shared workspaces in London and Berkeley where fellows will work from and mentors will visit. We are also open to remote fellows in the UK, US, or Canada. We will ask you about your availability to work from Berkeley or London (full- or part-time) during the program.</p>\n<p>Visa Sponsorship: We are not currently able to sponsor visas for fellows. To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program.</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_5fba9d7d-674","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/5030244008","x-work-arrangement":"remote","x-experience-level":"entry","x-job-type":"full-time","x-salary-range":"3,850 USD / 2,310 GBP / 4,300 CAD per week","x-skills-required":["Python programming","AI security","Cybersecurity","Empirical research","Machine learning","Deep learning","Experiment management"],"x-skills-preferred":["Open-source projects","Pentesting","Vulnerability research","Offensive security","CVEs","Bug bounty vulnerabilities","Empirical ML research projects","Deep learning frameworks"],"datePosted":"2026-03-08T13:52:43.813Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python programming, AI security, Cybersecurity, Empirical research, Machine learning, Deep learning, Experiment management, Open-source projects, Pentesting, Vulnerability research, Offensive security, CVEs, Bug bounty vulnerabilities, Empirical ML research projects, Deep learning frameworks","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":2310,"maxValue":4300,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c76d0c6d-ec7"},"title":"Technical Policy Manager, Cyber Harms","description":"<p><strong>About the Role:</strong></p>\n<p>We are looking for a cybersecurity expert to lead our efforts to prevent AI misuse in the cyber domain. As a Cyber Harms Technical Policy Manager, you will lead a team applying deep technical expertise to inform the design of safety systems that detect harmful cyber behaviours and prevent misuse by sophisticated threat actors.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Lead and grow a team of technical specialists focused on cyber threat modelling and evaluation frameworks</li>\n<li>Design and oversee execution of capability evaluations (&#39;evals&#39;) to assess the cyber-relevant capabilities of new models</li>\n<li>Create comprehensive cyber threat models, including attack vectors, exploit chains, precursor identification, and weaponization techniques</li>\n<li>Develop and iterate on usage policies that govern responsible use of our models for emerging capabilities and use cases related to cyber harms</li>\n<li>Serve as the primary domain expert on cyber harms, advising cross-functional teams on threat landscapes and mitigation strategies</li>\n<li>Collaborate closely with internal and external threat modelling experts to develop training data for safety systems, and with ML engineers to train these systems, optimising for both robustness against adversarial attacks and low false-positive rates for legitimate security researchers</li>\n<li>Analyse safety system performance in traffic, identifying gaps and proposing improvements</li>\n<li>Conduct regular reviews of existing policies and enforcement systems to identify and address gaps and ambiguities related to cybersecurity risks</li>\n<li>Develop rigorous stress-testing of safeguards against evolving cyber threats and product surfaces</li>\n<li>Partner with Research, Product, Policy, Security Team, and Frontier Red Team to ensure cybersecurity safety is embedded throughout the model development lifecycle</li>\n<li>Translate cybersecurity domain knowledge into actionable safety requirements and clearly articulated policies</li>\n<li>Contribute to external communications, including model cards, blog posts, and policy documents related to cybersecurity safety</li>\n<li>Monitor emerging technologies and threat landscapes for their potential to contribute to new risks and mitigation strategies, and strategically address these</li>\n<li>Mentor and develop team members, fostering a culture of technical excellence and responsible AI development</li>\n</ul>\n<p><strong>You may be a good fit if you have:</strong></p>\n<ul>\n<li>An M.S. or PhD in Computer Science, Cybersecurity, or a related technical field, OR equivalent professional experience in offensive or defensive cybersecurity</li>\n<li>5+ years of hands-on experience in cybersecurity, with deep expertise in areas such as vulnerability research, exploit development, network security, malware analysis, or penetration testing</li>\n<li>2+ years of experience managing technical teams or leading complex technical projects with multiple stakeholders</li>\n<li>Experience in scientific computing and data analysis, with proficiency in programming (Python preferred)</li>\n<li>Deep expertise in modern cybersecurity, including both offensive techniques (vulnerability research, exploit development, penetration testing, malware analysis) and defensive measures (detection, monitoring, incident response)</li>\n<li>Demonstrated ability to create threat models and translate technical cyber risks into policy frameworks</li>\n<li>Familiarity with responsible disclosure practices, vulnerability coordination, and cybersecurity frameworks (e.g., MITRE ATT&amp;CK, NIST Cybersecurity Framework, CWE/CVE systems)</li>\n<li>Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders</li>\n<li>Experience developing policies or guidelines at scale, balancing safety concerns with enabling legitimate use cases</li>\n<li>A passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies</li>\n<li>Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve</li>\n<li>Track record of translating specialised technical knowledge into actionable safety policies or enforcement guidelines</li>\n</ul>\n<p><strong>Preferred Qualifications:</strong></p>\n<ul>\n<li>Background in AI/ML systems, particularly experience with large language models</li>\n<li>Experience developing ML-based security systems or adversarial ML research</li>\n<li>Experience working with defence, intelligence, or security organisations (e.g., NSA, CISA, national labs, security contractors)</li>\n<li>Published security research, disclosed vulnerabilities, or participated in bug bounty programs</li>\n<li>Understanding of Trust &amp; Safety operations and content moderation at scale</li>\n<li>Certifications such as OSCP, OSCE, GXPN, or equivalent demonstrating technical depth</li>\n<li>Understanding of dual-use security research concerns and ethical considerations in AI safety</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_c76d0c6d-ec7","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/5066981008","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"The annual compensation for this role is not specified in the job posting.","x-skills-required":["cybersecurity","vulnerability research","exploit development","network security","malware analysis","penetration testing","scientific computing","data analysis","programming (Python)","threat modelling","policy frameworks","responsible disclosure practices","vulnerability coordination","cybersecurity frameworks (e.g., MITRE ATT&CK, NIST Cybersecurity Framework, CWE/CVE systems)"],"x-skills-preferred":["AI/ML systems","large language models","ML-based security systems","adversarial ML research","defence, intelligence, or security organisations","NSA, CISA, national labs, security contractors","published security research","disclosed vulnerabilities","bug bounty programs","Trust & Safety operations","content moderation at scale","OSCP, OSCE, GXPN, or equivalent certifications","dual-use security research concerns","ethical considerations in AI safety"],"datePosted":"2026-03-08T13:50:25.823Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, Washington, DC"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"cybersecurity, vulnerability research, exploit development, network security, malware analysis, penetration testing, scientific computing, data analysis, programming (Python), threat modelling, policy frameworks, responsible disclosure practices, vulnerability coordination, cybersecurity frameworks (e.g., MITRE ATT&CK, NIST Cybersecurity Framework, CWE/CVE systems), AI/ML systems, large language models, ML-based security systems, adversarial ML research, defence, intelligence, or security organisations, NSA, CISA, national labs, security contractors, published security research, disclosed vulnerabilities, bug bounty programs, Trust & Safety operations, content moderation at scale, OSCP, OSCE, GXPN, or equivalent certifications, dual-use security research concerns, ethical considerations in AI safety"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_cf63279d-d28"},"title":"Research Engineer, Reward Models Platform","description":"<p><strong>About the role</strong></p>\n<p>You will deeply understand the research workflows of our Finetuning teams and automate the high-friction parts – turning days of manual experimentation into hours. You’ll build the tools and infrastructure that enable researchers across the organisation to develop, evaluate, and optimise reward signals for training our models. Your scalable platforms will make it easy to experiment with different reward methodologies, assess their robustness, and iterate rapidly on improvements to help the rest of Anthropic train our reward models.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and build infrastructure that enables researchers to rapidly iterate on reward signals, including tools for rubric development, human feedback data analysis, and reward robustness evaluation</li>\n<li>Develop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologies</li>\n<li>Create tooling that allows researchers to easily compare different reward methodologies (preference models, rubrics, programmatic rewards) and understand their effects</li>\n<li>Build pipelines and workflows that reduce toil in reward development, from dataset preparation to evaluation to deployment</li>\n<li>Implement monitoring and observability systems to track reward signal quality and surface issues during training runs</li>\n<li>Collaborate with researchers to translate science requirements into platform capabilities</li>\n<li>Optimise existing systems for performance, reliability, and ease of use</li>\n<li>Contribute to the development of best practices and documentation for reward development workflows</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have prior research experience</li>\n<li>Are excited to work closely with researchers and translate ambiguous requirements into well-scoped engineering projects</li>\n<li>Have strong Python skills</li>\n<li>Have experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms</li>\n<li>Are comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing tooling</li>\n<li>Can balance building robust, maintainable systems with the need to move quickly in a research environment</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>Care about the societal impacts of your work and are motivated by Anthropic&#39;s mission to develop safe AI</li>\n</ul>\n<p><strong>Strong candidates may also have experience with</strong></p>\n<ul>\n<li>Experience with ML research</li>\n<li>Building internal tooling and platforms for ML researchers</li>\n<li>Data quality assessment and pipeline optimisation</li>\n<li>Experiment tracking, evaluation frameworks, or MLOps tooling</li>\n<li>Large-scale data processing (e.g., Spark, Hive, or similar)</li>\n<li>Kubernetes, distributed systems, or cloud infrastructure</li>\n<li>Familiarity with reinforcement learning or fine-tuning workflows</li>\n</ul>\n<p><strong>Representative projects</strong></p>\n<ul>\n<li>Building infrastructure that allows researchers to rapidly test new rubric designs against small models before scaling up</li>\n<li>Developing automated systems to detect reward hacks and surface problematic behaviours during training</li>\n<li>Creating tooling for comparing different grading methodologies and understanding their effects on model behaviour</li>\n<li>Building a data quality flywheel that helps researchers identify problematic transcripts and feed improvements back into the system</li>\n<li>Developing dashboards and monitoring systems that give researchers visibility into reward signal quality across training runs</li>\n<li>Streamlining dataset preparation workflows to reduce latency and operational overhead</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with the process.</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_cf63279d-d28","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/5024831008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $500,000 USD","x-skills-required":["Python","ML workflows","data pipelines","infrastructure/tooling/platforms","distributed systems","cloud infrastructure","reinforcement learning","fine-tuning workflows"],"x-skills-preferred":["ML research","data quality assessment","pipeline optimisation","experiment tracking","evaluation frameworks","MLOps tooling","large-scale data processing","Kubernetes"],"datePosted":"2026-03-08T13:48:05.218Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, Seattle, WA, New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, ML workflows, data pipelines, infrastructure/tooling/platforms, distributed systems, cloud infrastructure, reinforcement learning, fine-tuning workflows, ML research, data quality assessment, pipeline optimisation, experiment tracking, evaluation frameworks, MLOps tooling, large-scale data processing, Kubernetes","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_eddc20bf-1ad"},"title":"Research Engineer, Codex","description":"<p><strong>Research Engineer, Codex</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>$380K – $445K • Offers Equity</li>\n</ul>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.</p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p>More details about our benefits are available to candidates during the hiring process.</p>\n<p>This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.</p>\n<p><strong><strong>About the Team</strong></strong></p>\n<p>The Codex team is responsible for building state-of-the-art AI systems that can write code, reason about software, and act as intelligent agents for developers and non-developers alike. Our mission is to push the frontier of code generation and agentic reasoning, and deploy these capabilities in real-world products such as ChatGPT and the API, as well as in next-generation tools specifically designed for agentic coding. We operate across research, engineering, product, and infrastructure—owning the full lifecycle of experimentation, deployment, and iteration on novel coding capabilities.</p>\n<p><strong><strong>About the Role</strong></strong></p>\n<p>As a member of the Codex team, you will advance the capabilities, performance, and reliability of AI coding models through a combination of research, experimentation, and system optimization. You’ll collaborate with world-class researchers and engineers to develop and deploy systems that help millions of users write better code, faster—while also ensuring these systems are efficient, cost-effective, and production-ready.</p>\n<p>We’re looking for people who combine deep curiosity, strong technical fundamentals, and a bias toward impact. Whether your strengths lie in ML research, systems engineering, or performance optimization, you’ll play a pivotal role in pushing the state of the art and bringing these advances into the hands of real users.</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><strong>In this role, you might:</strong></strong></p>\n<ul>\n<li>Design and run experiments to improve code generation, reasoning, and agentic behavior in Codex models.</li>\n</ul>\n<ul>\n<li>Develop research insights into model training, alignment, and evaluation.</li>\n</ul>\n<ul>\n<li>Hunt down and address inefficiencies across the Codex system stack—from agent behavior to LLM inference to container orchestration—and land high-leverage performance improvements.</li>\n</ul>\n<ul>\n<li>Build tooling to measure, profile, and optimize system performance at scale.</li>\n</ul>\n<ul>\n<li>Work across the stack to prototype new capabilities, debug complex issues, and ship improvements to production.</li>\n</ul>\n<p><strong><strong>You might thrive in this role if you:</strong></strong></p>\n<ul>\n<li>Are excited to explore and push the boundaries of large language models, especially in the domain of software reasoning and code generation.</li>\n</ul>\n<ul>\n<li>Have strong software engineering skills and enjoy quickly turning ideas into working prototypes.</li>\n</ul>\n<ul>\n<li>Think holistically about performance, balancing speed, cost, and user experience.</li>\n</ul>\n<ul>\n<li>Bring creativity and rigor to open-ended research problems and thrive in highly iterative, ambiguous environments.</li>\n</ul>\n<ul>\n<li>Have experience operating across both ML systems and cloud infrastructure.</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_eddc20bf-1ad","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/793964ae-d40b-45e3-9798-84f4b6da48c5","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$380K – $445K • Offers Equity","x-skills-required":["ML research","systems engineering","performance optimization","software engineering","cloud infrastructure"],"x-skills-preferred":["large language models","software reasoning","code generation","agentic reasoning"],"datePosted":"2026-03-06T18:41:00.782Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"ML research, systems engineering, performance optimization, software engineering, cloud infrastructure, large language models, software reasoning, code generation, agentic reasoning","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":380000,"maxValue":445000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1db86d95-013"},"title":"Researcher, Frontier Biological and Chemical Risks","description":"<p><strong>Job Posting</strong></p>\n<p><strong>Researcher, Frontier Biological and Chemical Risks</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>Estimated Base Salary $295K – $445K</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 Preparedness team is an important part of the Safety Systems org at OpenAI, and is guided by OpenAI’s Preparedness Framework.</p>\n<p>Frontier AI models have the potential to benefit all of humanity, but also pose increasingly severe risks. To ensure that AI promotes positive change, the Preparedness team helps us prepare for the development of increasingly capable frontier AI models. This team is tasked with identifying, tracking, and preparing for catastrophic risks related to frontier AI models.</p>\n<p>The mission of the Preparedness team is to:</p>\n<ol>\n<li>Closely monitor and predict the evolving capabilities of frontier AI systems, with an eye towards misuse risks whose impact could be catastrophic to our society</li>\n</ol>\n<ol>\n<li>Ensure we have concrete procedures, infrastructure and partnerships to mitigate these risks and to safely handle the development of powerful AI systems</li>\n</ol>\n<p>Preparedness tightly connects capability assessment, evaluations, and internal red teaming, and mitigations for frontier models, as well as overall coordination on AGI preparedness. This is fast paced, exciting work that has far reaching importance for the company and for society.</p>\n<p><strong>About the Role</strong></p>\n<p>We are looking to hire exceptional research engineers that can push the boundaries of our frontier models. Specifically, we are looking for those that will help us shape our empirical grasp of the whole spectrum of AI safety concerns and will own individual threads within this endeavor end-to-end.</p>\n<p>You will own the scientific validity of our frontier preparedness capability evaluations—designing new evals grounded in real threat models (including high-consequence domains like CBRN as well as cyber and other frontier-risk areas), and maintaining existing evals so they don’t stale or silently regress. You’ll define datasets, graders, rubrics, and threshold guidance, and produce auditable artifacts (evaluation cards, capability reports, system-card inputs) that leadership can trust during high-stakes launches.</p>\n<p><strong>In this role, you&#39;ll:</strong></p>\n<ul>\n<li>Work on identifying emerging AI safety risks and new methodologies for exploring the impact of these risks</li>\n</ul>\n<ul>\n<li>Build (and then continuously refine) evaluations of frontier AI models that assess the extent of identified risks</li>\n</ul>\n<ul>\n<li>Design and build scalable systems and processes that can support these kinds of evaluations</li>\n</ul>\n<ul>\n<li>Contribute to the refinement of risk management and the overall development of “best practice” guidelines for AI safety evaluations</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Are passionate and knowledgeable about short-term and long-term AI safety risks</li>\n</ul>\n<ul>\n<li>Demonstrate the ability to think outside the box and have a robust “red-teaming mindset”</li>\n</ul>\n<ul>\n<li>Have experience in ML research engineering, ML observability and monitoring, creating large language model-enabled applications, and/or another technical domain applicable to AI risk</li>\n</ul>\n<ul>\n<li>Are able to operate effectively in a dynamic and extremely fast-paced research environment as well as scope and deliver projects end-to-end</li>\n</ul>\n<p><strong>It would be great if you also have:</strong></p>\n<ul>\n<li>First-hand experience in red-teaming systems—be it computer systems or otherwise</li>\n</ul>\n<ul>\n<li>A good understanding of the (nuances of) societal aspects of AI deployment</li>\n</ul>\n<ul>\n<li>Excellent communication skills and the ability to work cross-functionally</li>\n</ul>\n<p>_This role may require access to technology or technical data controlled under the U.S. Export Administration Regulations or International Traffic in Arms Regulations. Therefore, this role is restricted to individuals described in paragraph (a)(1) of the definition of “U.S. person” in the U.S. Export Administration Regulations, 15 C.F.R. § 772.1, and in the International Traffic in Arms Regulations, 22 C.F.R. § 120.62. U.S. persons are U.S. citizens, U.S. legal permanent residents, individuals granted asylum status in the United States, and individuals who are not U.S. citizens but are lawfully admitted for permanent residence in the United States._</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_1db86d95-013","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/3fc46cbc-7e5a-4edc-96dc-ca433e76d181","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$295K – $445K","x-skills-required":["ML research engineering","ML observability and monitoring","creating large language model-enabled applications","AI risk","red-teaming systems","societal aspects of AI deployment"],"x-skills-preferred":["first-hand experience in red-teaming systems","excellent communication skills","ability to work cross-functionally"],"datePosted":"2026-03-06T18:40:56.981Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"ML research engineering, ML observability and monitoring, creating large language model-enabled applications, AI risk, red-teaming systems, societal aspects of AI deployment, first-hand experience in red-teaming systems, excellent communication skills, ability to work cross-functionally","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":295000,"maxValue":445000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_b0c58796-627"},"title":"Research Engineer, Frontier Evals & Environments","description":"<p><strong>Job Posting</strong></p>\n<p>Research Engineer, Frontier Evals &amp; Environments</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>$205K – $380K • Offers Equity</li>\n</ul>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. 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This team builds ambitious environments to measure and steer our models, and creates self-improvement loops to steer our training, safety, and launch decisions. Some of the team&#39;s open-sourced evaluations include GDPval, SWE-bench Verified, MLE-bench, PaperBench, and SWE-Lancer, and the team built and ran frontier evaluations for GPT4o, o1, o3, GPT 4.5, ChatGPT Agent, and GPT5. If you are interested in feeling firsthand the fast progress of our models, and steering them towards good, this is the team for you.</p>\n<p><strong>About you</strong></p>\n<p>We seek exceptional research engineers that can push the boundaries of our frontier models. Specifically, we are looking for those that will help us shape our empirical grasp of the whole spectrum of AI capabilities measurement and will own individual threads within this endeavor end-to-end.</p>\n<p><strong>In this role, you&#39;ll:</strong></p>\n<ul>\n<li>Create ambitious RL environments to push our models to their limits</li>\n</ul>\n<ul>\n<li>Work on measuring frontier model capabilities, skills, and behaviors</li>\n</ul>\n<ul>\n<li>Develop new methodologies for automatically exploring the behavior of these models</li>\n</ul>\n<ul>\n<li>Help steer training for our largest training runs, and see the future first</li>\n</ul>\n<ul>\n<li>Design scalable systems and processes to support continuous evaluation</li>\n</ul>\n<ul>\n<li>Build self-improvement loops to automate model understanding</li>\n</ul>\n<p><strong>We expect you to be:</strong></p>\n<ul>\n<li>Passionate and knowledgeable about AGI/ASI measurement</li>\n</ul>\n<ul>\n<li>Strong engineering and statistical analysis skills</li>\n</ul>\n<ul>\n<li>Able to think outside the box and have a robust “red-teaming mindset”</li>\n</ul>\n<ul>\n<li>Experienced in ML research engineering, stochastic systems, observability and monitoring, LLM-enabled applications, and/or another technical domain applicable to AI evaluations</li>\n</ul>\n<ul>\n<li>Able to operate effectively in a dynamic and extremely fast-paced research environment as well as scope and deliver projects end-to-end</li>\n</ul>\n<p><strong>It would be great if you also have:</strong></p>\n<ul>\n<li>First-hand experience in red-teaming systems—be it computer systems or otherwise</li>\n</ul>\n<ul>\n<li>An ability to work cross-functionally</li>\n</ul>\n<ul>\n<li>Excellent communication skills</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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Achieving this requires bringing the world’s most exceptional talent under one roof to push the boundaries of what’s possible.</p>\n<p>Our Research Recruiting team plays a critical role in this effort. We are an embedded part of the research organization, working side by side with our research staff to deeply understand evolving priorities, build trust, and strategically shape the future of OpenAI’s talent.</p>\n<p><strong>About the Role</strong></p>\n<p>You will own and execute long-term talent strategies to identify, engage, and recruit many of the world’s leading and emerging AI researchers, research engineers, and technical scientists working at the frontier of machine learning. 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You will play a critical role in managing the team, ensuring product quality and reliability, and establishing processes that foster collaboration and efficiency.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Manage and grow a high-performing engineering team in a fast-paced, high-expectation environment.</li>\n</ul>\n<ul>\n<li>Ramp up on the team’s technology, product, and business goals, including hands-on work to develop a deep technical understanding.</li>\n</ul>\n<ul>\n<li>Lead hiring and onboarding efforts to build a strong, collaborative team.</li>\n</ul>\n<ul>\n<li>Set clear priorities and strategic direction in collaboration with engineering, product, design, and research teams.</li>\n</ul>\n<ul>\n<li>Drive process improvements to increase team effectiveness and product quality.</li>\n</ul>\n<ul>\n<li>Deliver complex, cross-functional projects that align with business objectives and drive impact.</li>\n</ul>\n<ul>\n<li>Ensure the Atlas browser is scalable, reliable, and continuously improving.</li>\n</ul>\n<ul>\n<li>Collaborate closely with Product and Research teams to push the boundaries of AI-enabled assistance.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Have 5+ years of experience managing engineering teams in a high-performance, fast-paced environment.</li>\n</ul>\n<ul>\n<li>Bring 5+ years of experience building and operating distributed systems at scale OR 5+ years of experience building installed/native apps at scale.</li>\n</ul>\n<ul>\n<li>Take pride in building delightful, high quality product experiences</li>\n</ul>\n<ul>\n<li>Have a strong track record of leading world-class teams and delivering impactful projects.</li>\n</ul>\n<ul>\n<li>Care deeply about diversity, equity, and inclusion, and have a track record of building inclusive teams</li>\n</ul>\n<ul>\n<li>Thrive in ambiguity and can adapt quickly to rapidly changing conditions.</li>\n</ul>\n<ul>\n<li>Have experience closing competitive candidates for your team, and the ability to craft and convey compelling visions of the future</li>\n</ul>\n<ul>\n<li>Have a strong coaching mindset and excel at guiding engineers to reach their peak potential.</li>\n</ul>\n<ul>\n<li>Have a voracious and intrinsic desire to learn and fill in missing skills—and an equally strong talent for sharing learnings clearly and concisely with others</li>\n</ul>\n<ul>\n<li>Are comfortable navigating AI/ML research collaborations (nice to have).</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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