About Mercor

Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise.

We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows.

Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence.

Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London.

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