Research Engineer - Environments, Data and Post-Training
San Francisco • FullTime
Posted 2h ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
AI Research Engineer
About the job
Mercor is a leading AI data company that organizes human intelligence to power the AI economy. We are building the layer between human expertise and frontier models, enabling millions of domain experts to train AI models. As a Research Scientist, 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. Your work will influence frontier models, Mercor’s products, and the broader research community through publications and technical reports.
Responsibilities
- Implement novel post-training methods to 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 (e.g., GRPO, 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 to inform training decisions.
- Translate open-ended research questions into rigorous experiments and production systems.
- Collaborate with researchers, applied AI teams, engineers, and domain experts on training data production.
- Contribute to open-source post-training tools and research.
Requirements
- Demonstrated experience training and evaluating machine learning models.
- 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.
- Experience on an industry post-training or frontier-model team (nice to have).
- Main authorship of publications at top-tier conferences (NeurIPS, ICML, ACL) (nice to have).
- Experience with synthetic-data generation (nice to have).
- Experience building large-scale evaluation or data-generation infrastructure (nice to have).
- Solid foundations in distributed or backend systems, and experimental design (nice to have).
- Familiarity with APIs, databases, and cloud infrastructure (nice to have).
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