{"schemaVersion":"jobsearcher.job.v1","id":"d0202d110a1cafbacb96cc9e","url":"https://jobsearcher.com/jobs/d0202d110a1cafbacb96cc9e","canonicalUrl":"https://jobsearcher.com/jobs/d0202d110a1cafbacb96cc9e","title":"Research Engineer PostTraining Data","description":"Research Engineer, Post-Training Data\r\nLocation: San FranEmployment Type:Full-time\r\nFocus:Post-Training, Reinforcement Learning, Data Generation, Research Environments, Frontier AI\r\nProfile:Researcher-engineer with strong ML and software engineering depthAbout Our Client\r\nOur client is building the next generation of post-training data infrastructure for frontier AI labs.The company’s core belief is that research and data production are inseparable. The best post-training data is not created through brute-force labeling or headcount alone — it requires domain expertise, research judgment, ML fluency, and the ability to build systems that scale data generation superlinearly.Inbound demand from frontier AI customers is growing faster than the current team can support, and our client is hiring early technical talent to help meet that demand.This is an opportunity to join a team building high-quality post-training data, reinforcement learning environments, long-horizon tasks, and domain-specific evaluation workflows for some of the most advanced AI systems in the world.About the Role\r\nOur client is hiring aResearch Engineer, Post-Training Datato own the full lifecycle of post-training model and data work.This role blends AI research, ML engineering, software engineering, and data production into one function. The ideal candidate is not just a researcher and not just an engineer — they are someone who can understand a domain deeply, identify what makes a task realistic and economically valuable, build the environment or data‑generation system, and improve the model through high-quality post-training data.The company is indexing heavily on research and ML horsepower, strong software ability, high slope, and data taste.What You’ll DoOwn post-training workflows end to end, from infrastructure provisioning through data generation and curationBuild and curate high-quality post-training data from model and environment generation processesProduce reinforcement learning environments and long-horizon tasks across complex domainsWork on research-loop, science, chip, physics, and other technically deep task environmentsBuild systems that scale data generation superlinearly through self-improving developer processesImprove data generation through better tools, workflows, automation, evaluation, and model feedback loopsExercise and develop strong data taste around what problems matter in a given domainIdentify realistic, economically valuable tasks and environments that frontier AI labs will actually wantBlend research and data production into a single technical functionDebug ML pipelines, understand unfamiliar code quickly, and solve open-ended technical problemsHelp define the standards for what high-quality post-training data should look like across domainsWhat We’re Looking ForGenuine spike in ML, AI research, or a technical research domainExperience or strong interest in post-training, reinforcement learning, evaluations, interpretability, or related areasStrong software engineering ability and comfort building production-quality tools or research systemsAbility to post-train models and build or curate high-quality post‑training dataStrong judgment around data quality, task design, and what makes a problem valuable to AI labsFast problem‑solving ability and strong code comprehensionAbility to work across unfamiliar domains and ramp quicklyHigh‑slope learning profile with strong technical curiosityComfort operating in ambiguous, research‑heavy environmentsEvidence of deep commitment, strong output, and public artifacts or meaningful technical workIdeal Background\r\nOur client is especially interested in candidates who combine:Domain or research expertise in a field such as neuroscience, physics, chemistry, systems, chip design, science, or another technical disciplineStrong ML or software engineering coreExperience with PyTorch, ML pipelines, model debugging, or research toolingExperience building environments, evaluations, or data‑generation systemsExposure to post‑training, RL, long‑horizon tasks, or frontier model workflowsPedigree can be a helpful signal, but it is not the primary filter. The team cares more about slope, research horsepower, problem‑solving speed, and the ability to build.Bonus ExperienceExperience at frontier AI labs, AI infrastructure companies, or high‑talent technical teamsExperience with post‑training data, RL environments, evals, model behavior, or interpretabilityExperience building data products, research tooling, or developer workflows for AI teamsExperience in domains like chip design, physics, chemistry, biology, systems, or scientific computingPublic artifacts, research projects, open‑source work, technical writing, or demos that show exceptional abilityExperience creating tasks or environments that require long‑horizon reasoningExperience scaling data generation through automation rather than manual laborWho Will Thrive HereA researcher‑engineer who treats data as a research problemA high‑slope polymath with a real technical spikeSomeone who can figure out unfamiliar domains quicklyA builder who understands that the best data comes from strong research judgmentSomeone obsessed with creating realistic, economically valuable AI training and evaluation environmentsA deeply committed, results‑driven technical operatorSomeone excited by frontier lab customers and the thesis that better systems can scale data generation superlinearlyWhy This OpportunityJoin an early team serving fast-growing demand from frontier AI customersWork on the core bottleneck behind better post‑training outcomes: high-quality dataBuild RL environments and long‑horizon task systems across technically deep domainsShape how research and data production come together as one functionWork on systems designed to scale data generation through process, tooling, and model feedback — not simply more peopleBuild at the frontier of post‑training, evaluations, RL environments, and data qualityStep into a role where research taste, engineering speed, and domain curiosity all matterIdeal Candidate Profile\r\nThe ideal candidate is a research-minded engineer with a real spike in ML, AI research, or a technical domain.They can read and understand code quickly, debug ML pipelines, build tools, reason about task quality, and identify what makes a data environment valuable to a frontier AI lab. They are not looking for a narrow research role or a pure software role — they want to build the systems and data that make models better.This person is high‑slope, obsessive, technically broad, and excited to help define what great post‑training data looks like.#J-18808-Ljbffr","company":"Nxt Level","rawCompany":"nxt level","city":"Millbrae","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-10-14T01:38:08.181Z","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":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"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"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Research Engineer PostTraining Data","description":"Research Engineer, Post-Training Data\r\nLocation: San FranEmployment Type:Full-time\r\nFocus:Post-Training, Reinforcement Learning, Data Generation, Research Environments, Frontier AI\r\nProfile:Researcher-engineer with strong ML and software engineering depthAbout Our Client\r\nOur client is building the next generation of post-training data infrastructure for frontier AI labs.The company’s core belief is that research and data production are inseparable. The best post-training data is not created through brute-force labeling or headcount alone — it requires domain expertise, research judgment, ML fluency, and the ability to build systems that scale data generation superlinearly.Inbound demand from frontier AI customers is growing faster than the current team can support, and our client is hiring early technical talent to help meet that demand.This is an opportunity to join a team building high-quality post-training data, reinforcement learning environments, long-horizon tasks, and domain-specific evaluation workflows for some of the most advanced AI systems in the world.About the Role\r\nOur client is hiring aResearch Engineer, Post-Training Datato own the full lifecycle of post-training model and data work.This role blends AI research, ML engineering, software engineering, and data production into one function. The ideal candidate is not just a researcher and not just an engineer — they are someone who can understand a domain deeply, identify what makes a task realistic and economically valuable, build the environment or data‑generation system, and improve the model through high-quality post-training data.The company is indexing heavily on research and ML horsepower, strong software ability, high slope, and data taste.What You’ll DoOwn post-training workflows end to end, from infrastructure provisioning through data generation and curationBuild and curate high-quality post-training data from model and environment generation processesProduce reinforcement learning environments and long-horizon tasks across complex domainsWork on research-loop, science, chip, physics, and other technically deep task environmentsBuild systems that scale data generation superlinearly through self-improving developer processesImprove data generation through better tools, workflows, automation, evaluation, and model feedback loopsExercise and develop strong data taste around what problems matter in a given domainIdentify realistic, economically valuable tasks and environments that frontier AI labs will actually wantBlend research and data production into a single technical functionDebug ML pipelines, understand unfamiliar code quickly, and solve open-ended technical problemsHelp define the standards for what high-quality post-training data should look like across domainsWhat We’re Looking ForGenuine spike in ML, AI research, or a technical research domainExperience or strong interest in post-training, reinforcement learning, evaluations, interpretability, or related areasStrong software engineering ability and comfort building production-quality tools or research systemsAbility to post-train models and build or curate high-quality post‑training dataStrong judgment around data quality, task design, and what makes a problem valuable to AI labsFast problem‑solving ability and strong code comprehensionAbility to work across unfamiliar domains and ramp quicklyHigh‑slope learning profile with strong technical curiosityComfort operating in ambiguous, research‑heavy environmentsEvidence of deep commitment, strong output, and public artifacts or meaningful technical workIdeal Background\r\nOur client is especially interested in candidates who combine:Domain or research expertise in a field such as neuroscience, physics, chemistry, systems, chip design, science, or another technical disciplineStrong ML or software engineering coreExperience with PyTorch, ML pipelines, model debugging, or research toolingExperience building environments, evaluations, or data‑generation systemsExposure to post‑training, RL, long‑horizon tasks, or frontier model workflowsPedigree can be a helpful signal, but it is not the primary filter. The team cares more about slope, research horsepower, problem‑solving speed, and the ability to build.Bonus ExperienceExperience at frontier AI labs, AI infrastructure companies, or high‑talent technical teamsExperience with post‑training data, RL environments, evals, model behavior, or interpretabilityExperience building data products, research tooling, or developer workflows for AI teamsExperience in domains like chip design, physics, chemistry, biology, systems, or scientific computingPublic artifacts, research projects, open‑source work, technical writing, or demos that show exceptional abilityExperience creating tasks or environments that require long‑horizon reasoningExperience scaling data generation through automation rather than manual laborWho Will Thrive HereA researcher‑engineer who treats data as a research problemA high‑slope polymath with a real technical spikeSomeone who can figure out unfamiliar domains quicklyA builder who understands that the best data comes from strong research judgmentSomeone obsessed with creating realistic, economically valuable AI training and evaluation environmentsA deeply committed, results‑driven technical operatorSomeone excited by frontier lab customers and the thesis that better systems can scale data generation superlinearlyWhy This OpportunityJoin an early team serving fast-growing demand from frontier AI customersWork on the core bottleneck behind better post‑training outcomes: high-quality dataBuild RL environments and long‑horizon task systems across technically deep domainsShape how research and data production come together as one functionWork on systems designed to scale data generation through process, tooling, and model feedback — not simply more peopleBuild at the frontier of post‑training, evaluations, RL environments, and data qualityStep into a role where research taste, engineering speed, and domain curiosity all matterIdeal Candidate Profile\r\nThe ideal candidate is a research-minded engineer with a real spike in ML, AI research, or a technical domain.They can read and understand code quickly, debug ML pipelines, build tools, reason about task quality, and identify what makes a data environment valuable to a frontier AI lab. They are not looking for a narrow research role or a pure software role — they want to build the systems and data that make models better.This person is high‑slope, obsessive, technically broad, and excited to help define what great post‑training data looks like.#J-18808-Ljbffr","datePosted":"2026-10-14T01:38:08.181Z","dateModified":"2026-10-14T01:38:08.181Z","hiringOrganization":{"@type":"Organization","name":"Nxt Level","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"d0202d110a1cafbacb96cc9e"},"url":"https://jobsearcher.com/jobs/d0202d110a1cafbacb96cc9e"}}