Research Engineer Environments, Data and Post-Training

Mercors mission is to organize human intelligence to power the AI economy. Were 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. Mercors APEX benchmark family measures AIs 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. Youll 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.

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 Mercors products and the broader research community through releasing blogposts technical reports and papers.

• 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.

• 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.

• 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.

• 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

Required Experience:

IC