{"id":1052671,"url":"https://alion.io/job/mercor-research-engineer-environments-data-and-post-training-2","title":"Research Scientist - Environments, Data and Post-Training","company":{"id":4960,"name":"Mercor","domain":"mercor.com","url":"https://alion.io/company/mercor","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":89,"open_postings":12,"ghost_share":0,"stale_share":0.25,"repost_share":0,"time_to_fill_p50_days":76,"computed_at":"2026-11-11T05:49:22Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":250000,"max":500000,"currency":"USD","period":"year","gross":null,"usd_annual":500000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":true,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"GRPO","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Synthetic Data","optional":true}],"status":"live","first_seen_at":"2026-09-18T23:43:17Z","employer_posted_date":"2026-09-18","last_verified_at":"2026-11-12T02:08:17Z","board_verified":true,"closed_at":null,"days_open":54,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":54},"description":"About Mercor\nMercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.\nMercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.\nAbout the Role\nAs a Research Scientist at Mercor, you will work at the intersection of research and engineering on frontier post-training. You will develop new training and evaluation methods, test them through rigorous experiments, and implement successful approaches at scale.\nWorking with researchers, engineers, domain experts, and customers, you will investigate how data, rewards, environments, and optimization methods shape model behavior. Your work will influence frontier models, Mercor’s products, and the broader research community through releasing blogposts, technical reports, and papers.\nWhat You’ll Do\nImplement novel post-training methods that improve model reasoning, tool use, and agentic behavior.\n\nDevelop new training recipes for frontier open models.\n\nDesign and run experiments across datasets, reward functions, environments, and optimization strategies, including methods such as GRPO and DAPO.\n\nBuild reinforcement learning with verifiable rewards (RLVR) and other post-training pipelines at scale.\n\nInvestigate model capabilities and failure modes, then develop targeted training interventions.\n\nCreate methods for measuring data quality, usability, and causal impact on model performance.\n\nBuild scalable pipelines for data generation, filtering, augmentation, and selection.\n\nDevelop rubrics, evaluators, benchmarks, and scoring systems that inform training decisions.\n\nTranslate open-ended research questions into rigorous experiments and production systems.\n\nCollaborate with researchers, applied AI teams, engineers, and domain experts producing training data.\n\nContribute to open-source post-training tools and research.\n\nWhat We’re Looking For\nDemonstrated experience training and evaluating machine learning models.\n\nA strong research record in post-training, reinforcement learning, language-model evaluation, data-centric ML, or a closely related field.\n\nAbility to reason rigorously about model behavior, experimental results, and data quality.\n\nStrong programming skills and experience implementing machine learning systems.\n\nKnowledge of the current AI research landscape and important open problems.\n\nExcitement to work in person in San Francisco, five days a week (with optional remote Saturdays), and thrive in a high-intensity, high-ownership environment.\n\nNice To Have\nExperience on an industry post-training or frontier-model team.\n\nMain authorship of publications at top-tier conferences (NeurIPS, ICML, ACL).\n\nExperience with synthetic-data generation\n\nExperience building large-scale evaluation or data-generation infrastructure.\n\nSolid foundations in distributed or backend systems, and experimental design.\n\nFamiliarity with APIs, databases, and cloud infrastructure.\n\nBenefits\nBi-annual performance bonus structure\n\nGenerous equity grant vested over 4 years\n\nUp to $15k Relocation bonus\n\n$10K housing bonus (if you live within 0.5 miles of our office)\n\n$1.5K monthly stipend for meals\n\nFree Equinox membership\n\n$200 monthly laundry reimbursement\n\n$200 monthly personal wellness reimbursement\n\nHealth, Dental, Vision insurance","description_format":"text","description_chars":4081,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Vision 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