Research Engineer Intern in San Francisco

About Mercor

Mercor's mission is to organize human intelligence to power the AI economy. It is a leading AI data company that builds 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. Mercor's APEX benchmark family measures how AI performs on real professional work, and Mercor Enterprise brings the same infrastructure to Fortune 500 companies.

Mercor is a profitable Series C company valued at $10 billion. The team works in person five days a week in its San Francisco, NYC, or London offices.

About the Role

As a Research Engineer at Mercor, you will work at the intersection of engineering and applied AI research. You will contribute directly to post-training and RLVR, synthetic data generation, and large-scale evaluation workflows that affect frontier language models.

Your work will train large language models to master tool use, agentic behavior, and real-world reasoning in production environments. You will shape rewards, run post-training experiments, and build scalable systems that improve model performance. You will also help design and evaluate datasets, build data augmentation pipelines, and create rubrics and evaluators.

What You'll Do

• Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies affect model performance.
• Design and run reward-shaping experiments and algorithmic improvements (e.g., GRPO, DAPO) to improve LLM tool use, agentic behavior, and real-world reasoning.
• Measure data usability, quality, and performance uplift on key benchmarks.
• Build and maintain data generation and augmentation pipelines that scale with training needs.
• Create and refine rubrics, evaluators, and scoring frameworks that guide training and evaluation decisions.
• Build and operate LLM evaluation systems, benchmarks, and metrics at scale.
• Collaborate closely with AI researchers, applied AI teams, and experts producing training data.
• Work in a fast-paced, experimental research environment with rapid iteration cycles and high ownership.

What We're Looking For

• A strong applied research background, with a focus on post-training and/or model evaluation.
• Strong coding skills and hands-on experience with machine learning models.
• A solid understanding of data structures, algorithms, backend systems, and core engineering fundamentals.
• Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.
• The ability to reason about model behavior, experimental results, and data quality.
• Willingness to work in person in San Francisco five days a week (with optional remote Saturdays) in a high-intensity, high-ownership environment.

Nice To Have

• Real-world post-training team experience in industry (highest priority).
• Publications at top-tier conferences (NeurIPS, ICML, ACL).
• Experience training models or evaluating model performance.
• Experience in synthetic data generation, LLM evaluations, or RL-style workflows.
• Work samples, artifacts, or code repositories that demonstrate relevant skills.

Benefits

• $1.5K monthly stipend for meals
• Free Equinox membership
• $200 monthly laundry reimbursement
• $200 monthly personal wellness reimbursement