{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/empirical-ml-research-projects"},"x-facet":{"type":"skill","slug":"empirical-ml-research-projects","display":"Empirical Ml Research Projects","count":4},"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_1d579dbd-111"},"title":"Anthropic Fellows Program — AI Safety","description":"<p><strong>About the Role</strong></p>\n<p>The Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent - regardless of previous experience.</p>\n<p>As a fellow, you 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).</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>4 months of full-time research</li>\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 and security research community</li>\n<li>Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (these vary by country)</li>\n<li>Funding for compute (~$15k/month) and other research expenses</li>\n</ul>\n<p><strong>What to Expect</strong></p>\n<p>We will conduct an initial application &amp; reference check, technical assessments &amp; interviews, and a research discussion.</p>\n<p><strong>Strong Candidates</strong></p>\n<ul>\n<li>Are motivated by making sure AI is safe and beneficial for society as a whole</li>\n<li>Are excited to transition into empirical AI research and would be interested in a full-time role at Anthropic</li>\n<li>Have a strong technical background in computer science, mathematics, or physics</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>Unique Candidate Criteria</strong></p>\n<p>You might be a particularly great fit for this workstream if you:</p>\n<ul>\n<li>Are motivated by reducing catastrophic risks from advanced AI systems</li>\n<li>Have experience with empirical ML research projects</li>\n<li>Have experience working with large language models</li>\n<li>Have experience in one of the research areas mentioned above</li>\n<li>Have a track record of open-source contributions</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p>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><strong>Visa Sponsorship</strong></p>\n<p>We are not currently able to sponsor visas for fellows. To participate in the Fellows program, you need to have or independently obtain full-time work authorization in the UK, the US, or Canada.</p>\n<p><strong>Program Duration</strong></p>\n<p>The program runs for 4 months, full-time. If you can&#39;t commit to the full duration, please still apply and note your constraints in the application. We review these requests on a case-by-case basis.</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_1d579dbd-111","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.ai/","logo":"https://logos.yubhub.co/anthropic.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5183044008","x-work-arrangement":"remote|hybrid|onsite","x-experience-level":"entry|mid|senior|staff|executive","x-job-type":"full-time","x-salary-range":"3,850 USD / 2,310 GBP / 4,300 CAD per week","x-skills-required":["Python programming","Computer science","Mathematics","Physics"],"x-skills-preferred":["Empirical ML research projects","Large language models","Research areas mentioned above","Open-source contributions"],"datePosted":"2026-04-18T15:39:51.757Z","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|Research","industry":"Technology|AI","skills":"Python programming, Computer science, Mathematics, Physics, Empirical ML research projects, Large language models, Research areas mentioned above, Open-source contributions","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_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. 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