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Mercor is an AI data company building infrastructure between human expertise and frontier models. The company operates a platform where millions of domain experts train frontier AI models, and is expanding into enterprise with Mercor Enterprise for Fortune 500 companies. Mercor is a profitable Series C company valued at $10 billion.
As a Research Engineer, you will work at the intersection of research and engineering on frontier post-training methods. You'll develop novel training and evaluation approaches, test them through rigorous experiments, and implement successful methods at scale.
Key responsibilities include:
- Implementing novel post-training methods that improve model reasoning, tool use, and agentic behavior
- Developing new training recipes for frontier open models
- Designing and running experiments across datasets, reward functions, environments, and optimization strategies (GRPO, DAPO, etc.)
- Building reinforcement learning with verifiable rewards (RLVR) and other post-training pipelines at scale
- Investigating model capabilities and failure modes, then developing targeted training interventions
- Creating methods for measuring data quality, usability, and causal impact on model performance
- Building scalable pipelines for data generation, filtering, augmentation, and selection
- Developing rubrics, evaluators, benchmarks, and scoring systems that inform training decisions
- Translating open-ended research questions into rigorous experiments and production systems
- Collaborating with researchers, applied AI teams, engineers, and domain experts producing training data
- Contributing to open-source post-training tools and research
You'll work alongside researchers, operators, and AI companies at the forefront of shaping frontier systems. Your work will influence frontier models, Mercor's products, and the broader research community through publications, technical reports, and papers.
REQUIREMENTS:
- Demonstrated experience training and evaluating machine learning models
- Strong research record in post-training, reinforcement learning, language-model evaluation, data-centric ML, or 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 current AI research landscape and important open problems
- Willingness to work in-person in San Francisco five days a week (with optional remote Saturdays) and thrive in 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
