Research Scientist - Environments, Data and Post-Training

The role

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 experiments, and implement approaches at scale.

What you will 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.
• Build reinforcement learning with verifiable rewards and other post-training pipelines at scale.
• Investigate model capabilities and failure modes and 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.
• Translate open-ended research questions into rigorous experiments and production systems.
• Collaborate with researchers, applied AI teams, engineers, and domain experts.

Required qualifications

• 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).

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

About the company

Mercor builds infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. The company is profitable and recently raised a $750 million Series D at a $20 billion valuation.