{"schemaVersion":"jobsearcher.job.v1","id":"cc5084a25e8b97feb4dbef2d","url":"https://jobsearcher.com/jobs/cc5084a25e8b97feb4dbef2d","canonicalUrl":"https://jobsearcher.com/jobs/cc5084a25e8b97feb4dbef2d","title":"Research Engineer / Research Scientist","description":"About Us Preference Model is building automated ML research engineering. Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.\nAbout The Role Models of the future will be able to train themselves on tasks that they are not good at. We are interested in investigating how far we can push the boundaries of self-directed learning. We are looking for Research Engineers or Research Scientists to push the frontier of post-training on large language models in a role that blends research and engineering, requiring you to implement novel approaches and shape research directions.\nWhat You Will Do Train and evaluate models on our proprietary RL environments to validate data quality, surface gaps in task coverage, and close the feedback loop between environment design and model capability.\nArchitect and optimize our RL training infrastructure, from training abstractions to distributed experiment management, using frameworks like Verl, OpenRLHF, or similar. Help scale our systems to handle increasingly complex research workflows.\nDesign, implement, and test training environments, evaluations, and methodologies for RL agents.\nProfile and optimize training runs end-to-end, from data loading through reward computation, to maximize experiment throughput and shorten the research iteration cycle.\nWhat We are Looking For Experience running end-to-end LLM post-training pipelines\nProficiency in Python and PyTorch or JAX\nExperience with at least one modern RL training framework\nExperience building and operating ML infrastructure at scale\nYou may be a good fit if you also Have experience evaluating model outputs and building reward or evaluation signals\nStay current on post-training research and can translate papers into running code\nHave strong opinions (loosely held) about how to structure RL training code for reproducibility and fast iteration\nCan balance research exploration with engineering rigor\nHave strong systems design and communication skills\nWhat we offer Competitive cash and equity compensation (>90th percentile)\nOwnership and autonomy in a fast moving startup environment\nOpportunity to work with top machine learning engineers\nHealth, vision, dental, benefits\n401K match\nLunch provided everyday onsite\nWeekly snack orders\nVisa sponsorship & relocation support available\nWe value diverse perspectives and experiences. If you're excited about this role but don't check every box.\nCompensation Range: $200K - $350K\n\n#J-18808-Ljbffr","company":"Socket","rawCompany":"socket","city":"Seattle","state":"WA","isRemote":false,"isActive":false,"createdAt":"2026-08-04T03:24:57.459Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"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":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Research Engineer / Research Scientist","description":"About Us Preference Model is building automated ML research engineering. Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.\nAbout The Role Models of the future will be able to train themselves on tasks that they are not good at. We are interested in investigating how far we can push the boundaries of self-directed learning. We are looking for Research Engineers or Research Scientists to push the frontier of post-training on large language models in a role that blends research and engineering, requiring you to implement novel approaches and shape research directions.\nWhat You Will Do Train and evaluate models on our proprietary RL environments to validate data quality, surface gaps in task coverage, and close the feedback loop between environment design and model capability.\nArchitect and optimize our RL training infrastructure, from training abstractions to distributed experiment management, using frameworks like Verl, OpenRLHF, or similar. Help scale our systems to handle increasingly complex research workflows.\nDesign, implement, and test training environments, evaluations, and methodologies for RL agents.\nProfile and optimize training runs end-to-end, from data loading through reward computation, to maximize experiment throughput and shorten the research iteration cycle.\nWhat We are Looking For Experience running end-to-end LLM post-training pipelines\nProficiency in Python and PyTorch or JAX\nExperience with at least one modern RL training framework\nExperience building and operating ML infrastructure at scale\nYou may be a good fit if you also Have experience evaluating model outputs and building reward or evaluation signals\nStay current on post-training research and can translate papers into running code\nHave strong opinions (loosely held) about how to structure RL training code for reproducibility and fast iteration\nCan balance research exploration with engineering rigor\nHave strong systems design and communication skills\nWhat we offer Competitive cash and equity compensation (>90th percentile)\nOwnership and autonomy in a fast moving startup environment\nOpportunity to work with top machine learning engineers\nHealth, vision, dental, benefits\n401K match\nLunch provided everyday onsite\nWeekly snack orders\nVisa sponsorship & relocation support available\nWe value diverse perspectives and experiences. If you're excited about this role but don't check every box.\nCompensation Range: $200K - $350K\n\n#J-18808-Ljbffr","datePosted":"2026-08-04T03:24:57.459Z","dateModified":"2026-08-04T03:24:57.459Z","hiringOrganization":{"@type":"Organization","name":"Socket","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Seattle","addressRegion":"WA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"cc5084a25e8b97feb4dbef2d"},"url":"https://jobsearcher.com/jobs/cc5084a25e8b97feb4dbef2d"}}