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As a Research Engineer on the team, you&#39;ll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Build and improve the RL training infrastructure that researchers depend on day-to-day</li>\n<li>Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed</li>\n<li>Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster</li>\n<li>Own the reliability and performance of research runs end-to-end</li>\n<li>Contribute to design decisions that shape how Anthropic does RL at scale</li>\n</ul>\n<p>You may be a good fit if you</p>\n<ul>\n<li>Have strong software engineering fundamentals and a track record of building performant, reliable systems</li>\n<li>Have worked on ML infrastructure, distributed systems, or research tooling</li>\n<li>Care about enabling other people&#39;s work and find leverage through platforms rather than individual experiments</li>\n<li>Are comfortable operating across the stack, from low-level performance work to RL algorithms</li>\n<li>Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego</li>\n</ul>\n<p>Strong candidates may also have</p>\n<ul>\n<li>Experience with large-scale distributed training (RL, pre-training, or post-training)</li>\n<li>Familiarity with JAX, PyTorch, or similar ML frameworks</li>\n<li>A track record of operating at the edge of research and infra in a fast-moving environment</li>\n</ul>\n<p>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.</p>\n<p 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_116e18ff-e3a","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/5198108008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$500,000-$850,000 USD","x-skills-required":["software engineering","ML infrastructure","distributed systems","research tooling","JAX","PyTorch","large-scale distributed training"],"x-skills-preferred":["bias toward shipping and iterating quickly","high agency and low ego","operating at the edge of research and 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Here are some areas where you excel:</p>\n<ul>\n<li>You thrive in ambiguity and are a natural problem-solver. You are happy to navigate and solve new problems within a complex corporate environment.</li>\n<li>Whatever your role, you are usually the go-to person in your team to get stuff done when there is no obvious solution.</li>\n<li>You have a deep understanding of UX Research and what product teams need to be successful.</li>\n<li>You are highly organised and great at getting into the details, especially when it comes to compliance and data handling.</li>\n<li>You embrace new technologies and are adept at identifying and implementing tools that enhance productivity and insight sharing.</li>\n<li>You believe in fostering a culture of continuous learning and are passionate about helping teams become more customer-centric.</li>\n</ul>\n<p>As our UX Research Operations Manager, you will be the driving force behind creating a well-oiled research machine. You will own the setup of new processes, champion new technologies and be proactive in continuous improvement. Your responsibilities will span four key areas:</p>\n<p><strong>Participant management:</strong></p>\n<ul>\n<li>Identify and build key internal and external participant panels to ensure we have the right people to speak to.</li>\n<li>Establish and manage all compliance, privacy and process needs for participant recruitment.</li>\n<li>Take the lead on sourcing, evaluating and onboarding a dedicated participant recruitment tool.</li>\n</ul>\n<p><strong>Technology and insights:</strong></p>\n<ul>\n<li>Create and manage a central insights repository to ensure research is accessible, searchable and actionable.</li>\n<li>Take ownership of the UX Research tooling landscape, leveraging your understanding of platforms like Dovetail, Askable or similar tools to set the team up for success.</li>\n</ul>\n<p><strong>Ways of working:</strong></p>\n<ul>\n<li>Create valuable, scalable processes that support incorporating more UX Research into the Product Development Lifecycle.</li>\n<li>Design workflows that ensure customer insights are actually actioned by product teams rather than just reported.</li>\n<li>Jump in to solve operational challenges as they arise to enable teams to do their best work.</li>\n</ul>\n<p><strong>Collaboration and culture:</strong></p>\n<ul>\n<li>Partner closely with the Product Analytics Lead and the CX team to align qualitative research with quantitative data.</li>\n<li>Help embed, drive and grow a customer-centric culture across the wider organisation.</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<p>We&#39;re looking for someone who is willing to get stuck in and is comfortable taking the initiative in an ambiguous environment. The list below is not a checklist, so please do not be afraid to apply even if you only meet some of the bullet points. We are hiring for attitude over experience! We can help you get up to speed, and your learning and development will be supported by the Head of Product Operations.</p>\n<p><strong>Required</strong></p>\n<ul>\n<li>Either 2+ years of experience in a UX Research Ops role OR 5+ years of experience as a UX Researcher with demonstrable experience of improving the operations of your discipline (or equivalent experience).</li>\n<li>Deep understanding of UX Research methodologies and the end-to-end research process.</li>\n<li>Experience creating and managing participant pools.</li>\n<li>Experience creating or managing an insights repository.</li>\n</ul>\n<p><strong>Preferred</strong></p>\n<ul>\n<li>Experience operating in regulated, complex environments, including a strong understanding of how to appropriately handle participant and customer data.</li>\n<li>Familiarity with the UX Research tooling landscape</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_3d58b65d-e51","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Ford Credit Europe","sameAs":"https://www.fordcredit.com/","logo":"https://logos.yubhub.co/fordcredit.com.png"},"x-apply-url":"https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/62093","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["UX Research","UX Research Operations","Participant Management","Technology and Insights","Ways of Working","Collaboration and Culture"],"x-skills-preferred":["Dovetail","Askable","UX Research Tooling Landscape"],"datePosted":"2026-04-24T12:26:24.173Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Basildon"}},"employmentType":"FULL_TIME","occupationalCategory":"Operations","industry":"Finance","skills":"UX Research, UX Research Operations, Participant Management, Technology and Insights, Ways of Working, Collaboration and Culture, Dovetail, Askable, UX Research Tooling Landscape"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_80c3beb2-2c3"},"title":"Research Engineer, Machine Learning (RL Velocity)","description":"<p><strong>About the Role</strong></p>\n<p>As a Research Engineer on the RL Velocity team, you&#39;ll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Build and improve the RL training infrastructure that researchers depend on day-to-day</li>\n</ul>\n<ul>\n<li>Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed</li>\n</ul>\n<ul>\n<li>Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster</li>\n</ul>\n<ul>\n<li>Own the reliability and performance of research runs end-to-end</li>\n</ul>\n<ul>\n<li>Contribute to design decisions that shape how Anthropic does RL at scale</li>\n</ul>\n<p><strong>You May Be a Good Fit If You</strong></p>\n<ul>\n<li>Have strong software engineering fundamentals and a track record of building performant, reliable systems</li>\n</ul>\n<ul>\n<li>Have worked on ML infrastructure, distributed systems, or research tooling</li>\n</ul>\n<ul>\n<li>Care about enabling other people&#39;s work and find leverage through platforms rather than individual experiments</li>\n</ul>\n<ul>\n<li>Are comfortable operating across the stack, from low-level performance work to RL algorithms</li>\n</ul>\n<ul>\n<li>Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego</li>\n</ul>\n<p><strong>Strong Candidates May Also Have</strong></p>\n<ul>\n<li>Experience with large-scale distributed training (RL, pre-training, or post-training)</li>\n</ul>\n<ul>\n<li>Familiarity with JAX, PyTorch, or similar ML frameworks</li>\n</ul>\n<ul>\n<li>A track record of operating at the edge of research and infra in a fast-moving environment</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience</li>\n</ul>\n<ul>\n<li>Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience</li>\n</ul>\n<ul>\n<li>Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position</li>\n</ul>\n<ul>\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</ul>\n<ul>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive compensation and benefits</li>\n</ul>\n<ul>\n<li>Optional equity donation matching</li>\n</ul>\n<ul>\n<li>Generous vacation and parental leave</li>\n</ul>\n<ul>\n<li>Flexible working hours</li>\n</ul>\n<ul>\n<li>A lovely office space in which to collaborate with colleagues</li>\n</ul>\n<p><strong>How We&#39;re Different</strong></p>\n<ul>\n<li>We believe that the highest-impact AI research will be big science.</li>\n</ul>\n<ul>\n<li>At Anthropic, we work as a single cohesive team on just a few large-scale research efforts.</li>\n</ul>\n<ul>\n<li>We value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles.</li>\n</ul>\n<ul>\n<li>We view AI research as an empirical science, which has as much in common with 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