Research Engineer Intern - Environments, Data and Post-Training

The role

You will work at the intersection of engineering and applied AI research. You will contribute to post-training, RLVR, synthetic data generation, and large-scale evaluation workflows to impact frontier language models.

What you will do

• Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies impact model performance.
• Design and run reward-shaping experiments and algorithmic improvements to improve LLM tool-use, agentic behavior, and real-world reasoning.
• Quantify 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.
• Operate in a fast-paced, experimental research environment with rapid iteration cycles and high ownership.

Required qualifications

• Strong applied research background, with a focus on post-training and/or model evaluation.
• Strong coding proficiency and hands-on experience working with machine learning models.
• Strong understanding of data structures, algorithms, backend systems, and core engineering fundamentals.
• Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.
• Ability to reason deeply about model behavior, experimental results, and data quality.
• 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

• 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 demonstrating relevant skills

Benefits

• Health Benefits eligible at 30+ hours a week
• Overtime eligible at 1.5x from 40 to 55 hours; 2x over 55 hours-week
• $1.5K monthly stipend for meals
• Relocation & Housing Support
• Free Equinox membership
• $200 monthly laundry reimbursement
• $200 monthly personal wellness reimbursement

About the company

Mercor builds infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. The company is profitable and recently raised a $750 million Series D at a $20 billion valuation.