{"schemaVersion":"jobsearcher.job.v1","id":"e3219130b0fad14cd6bbb3ba","url":"https://jobsearcher.com/jobs/e3219130b0fad14cd6bbb3ba","canonicalUrl":"https://jobsearcher.com/jobs/e3219130b0fad14cd6bbb3ba","title":"Research Engineer","description":"About the Role\nWe're a fast-growing AI infrastructure company building the technical foundation for training and evaluating frontier AI agents. Our team includes International Olympiad medalists, serial AI startup founders, and researchers with publications at top venues (ICLR, NeurIPS, and similar). We're looking for Research Engineers to work across agent quality control automation, benchmarks, and synthetic data — shaping how AI agents learn and improve.\nThis is a high-ownership, high-impact role at an early-stage company. You'll work in ambiguous, fast-moving problem spaces alongside a tight-knit team where your contributions directly influence the trajectory of frontier AI development.\nWhat You'll Do\nBuild systems for creating new environments, improving data quality, and translating real-world workflows into tasks and benchmarks.\nBuild systems for creating, running, evaluating, and improving agent training environments.\nDesign experiments to understand model behavior, agent failure modes, and data quality issues.\nDevelop tools that help researchers, engineers, and data vendors create higher-quality tasks, trajectories, and feedback loops.\nWork across the full lifecycle of agent training data — from task design and environment setup to trajectory collection, evaluation, and validation.\nPartner with external vendors to identify bottlenecks and improve the quality and throughput of the data engine.\nBuild metrics and analyses to assess whether tasks, environments, and evals are genuinely useful for training frontier agents.\nWhat We're Looking For\nRequired:\n2–4 years of relevant engineering experience.\nProficiency in Python, Docker, and Linux environments.\nExperience with benchmarks and evals, including reasoning about task realism, rubric reliability, environment usability, and trajectory quality for RL training.\nStrong attention to detail — ability to spot subtle inconsistencies in data, model behavior, or task design.\nTrack record of building tools, pipelines, or research infrastructure with minimal guidance.\nEarly-stage startup experience; comfort working independently in fast-paced, ambiguous settings.\nExperience designing metrics and validation workflows.\nStrong quantitative or technical foundation, demonstrated through competitive programming, research, or independent project work.\nAbility to thrive in unstructured problem spaces and communicate clearly across time zones.\nNice to Have:\nBackground in reinforcement learning or AI alignment research.\nExperience working with large-scale data pipelines or vendor ecosystems.\nPublications or contributions to open-source ML/AI tooling.\nCompensation & Benefits\nSalary: $150,000 – $250,000 USD annually, depending on experience.\nVisa sponsorship is available.\nEquity participation in an early-stage, well-resourced AI company.\nLocation\nThis is an on-site role based in San Francisco, CA. Candidates should be prepared to work in-person with the team.","company":"Clera","rawCompany":"clera","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-05T09:44:08.427Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Research Engineer","description":"About the Role\nWe're a fast-growing AI infrastructure company building the technical foundation for training and evaluating frontier AI agents. Our team includes International Olympiad medalists, serial AI startup founders, and researchers with publications at top venues (ICLR, NeurIPS, and similar). We're looking for Research Engineers to work across agent quality control automation, benchmarks, and synthetic data — shaping how AI agents learn and improve.\nThis is a high-ownership, high-impact role at an early-stage company. You'll work in ambiguous, fast-moving problem spaces alongside a tight-knit team where your contributions directly influence the trajectory of frontier AI development.\nWhat You'll Do\nBuild systems for creating new environments, improving data quality, and translating real-world workflows into tasks and benchmarks.\nBuild systems for creating, running, evaluating, and improving agent training environments.\nDesign experiments to understand model behavior, agent failure modes, and data quality issues.\nDevelop tools that help researchers, engineers, and data vendors create higher-quality tasks, trajectories, and feedback loops.\nWork across the full lifecycle of agent training data — from task design and environment setup to trajectory collection, evaluation, and validation.\nPartner with external vendors to identify bottlenecks and improve the quality and throughput of the data engine.\nBuild metrics and analyses to assess whether tasks, environments, and evals are genuinely useful for training frontier agents.\nWhat We're Looking For\nRequired:\n2–4 years of relevant engineering experience.\nProficiency in Python, Docker, and Linux environments.\nExperience with benchmarks and evals, including reasoning about task realism, rubric reliability, environment usability, and trajectory quality for RL training.\nStrong attention to detail — ability to spot subtle inconsistencies in data, model behavior, or task design.\nTrack record of building tools, pipelines, or research infrastructure with minimal guidance.\nEarly-stage startup experience; comfort working independently in fast-paced, ambiguous settings.\nExperience designing metrics and validation workflows.\nStrong quantitative or technical foundation, demonstrated through competitive programming, research, or independent project work.\nAbility to thrive in unstructured problem spaces and communicate clearly across time zones.\nNice to Have:\nBackground in reinforcement learning or AI alignment research.\nExperience working with large-scale data pipelines or vendor ecosystems.\nPublications or contributions to open-source ML/AI tooling.\nCompensation & Benefits\nSalary: $150,000 – $250,000 USD annually, depending on experience.\nVisa sponsorship is available.\nEquity participation in an early-stage, well-resourced AI company.\nLocation\nThis is an on-site role based in San Francisco, CA. Candidates should be prepared to work in-person with the team.","datePosted":"2026-08-05T09:44:08.427Z","dateModified":"2026-08-05T09:44:08.427Z","hiringOrganization":{"@type":"Organization","name":"Clera","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"e3219130b0fad14cd6bbb3ba"},"url":"https://jobsearcher.com/jobs/e3219130b0fad14cd6bbb3ba"}}