Research Engineer - Environments, Data and Post-Training

About Mercor Mercor'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. Mercor 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. About the Role As 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. Working 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. What You’ll Do Implement novel post-training methods that improve model reasoning, tool use, and agentic behavior. Develop new training recipes for frontier open models. Design and run experiments across datasets, reward functions, environments, and optimization strategies, including methods such as GRPO and DAPO. Build reinforcement learning with verifiable rewards (RLVR) and other post-training pipelines at scale. Investigate model capabilities and failure modes, then develop targeted training interventions. Create methods for measuring data quality, usability, and causal impact on model performance. Build scalable pipelines for data generation, filtering, augmentation, and selection. Develop rubrics, evaluators, benchmarks, and scoring systems that inform training decisions. Translate open-ended research questions into rigorous experiments and production systems. Collaborate with researchers, applied AI teams, engineers, and domain experts producing training data. Contribute to open-source post-training tools and research. What We’re Looking For Demonstrated experience training and evaluating machine learning models. A strong research record in post-training, reinforcement learning, language-model evaluation, data-centric ML, or a closely related field. Ability to reason rigorously about model behavior, experimental results, and data quality. Strong programming skills and experience implementing machine learning systems. Knowledge of the current AI research landscape and important open problems. Excitement to work in person in San Francisco, five days a week (with optional remote Saturdays), and thrive in a high-intensity, high-ownership environment. Nice To Have Experience on an industry post-training or frontier-model team. Main authorship of publications at top-tier conferences (NeurIPS, ICML, ACL). Experience with synthetic-data generation Experience building large-scale evaluation or data-generation infrastructure. Solid foundations in distributed or backend systems, and experimental design. Familiarity with APIs, databases, and cloud infrastructure. Benefits Bi-annual performance bonus structure Generous equity grant vested over 4 years Up to $15k Relocation bonus $10K housing bonus (if you live within 0.5 miles of our office) $1.5K monthly stipend for meals Free Equinox membership $200 monthly laundry reimbursement $200 monthly personal wellness reimbursement Health, Dental, Vision insurance