About Mercor
Mercor is at the intersection of labor markets and AI research. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development.
Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $1.5 million a day.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll 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 new San Francisco headquarters.
About the Role
As a Research Engineer Intern at Mercor, you’ll work at the intersection of engineering and cutting-edge AI research. You’ll contribute directly to post-training and RLVR, data generation, and large-scale evaluation workflows. Your work will be used to train Large Language Models to master tool-use, agentic behavior, and real-world reasoning. You’ll shape rewards, experiment with algorithmic improvements (GRPO, DAPO, etc.), and enhance data quality to improve model performance in real production environments. You’ll help design and evaluate datasets, create scalable data augmentation pipelines, and build rubrics that push the boundaries of what LLMs can learn.
What You’ll Do
Work on post-training and RLVR pipelines to help Mercor understand how datasets impact 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.
Quantify data usability, quality, and uplift on key benchmarks.
Build data generation and augmentation pipelines that scale with training needs.
Create and refine rubrics, evaluators, and scoring frameworks that push the boundaries of what LLMs can learn.
Collaborate closely with research engineers, applied AI teams, and experts producing data.
Operate in a fast-paced, experimental research environment with rapid iteration cycles.
What We’re Looking For
Pursuing a degree in Computer Science or a related field (graduating 2025–2027); ability to start in early 2026 is strongly preferred.
Strong programming skills in Python, Go, or JavaScript, with an ability to write clean, reliable, production-grade code.
Understanding of data structures, algorithms, backend systems, and core engineering fundamentals.
Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.
Curiosity and passion for AI research, reinforcement learning, and fast-moving startups.
Excitement to work in person and thrive in a high-intensity, high-ownership engineering environment.
Nice To Have
Real-world post-training team experience in industry
Work samples, artifacts, or code repositories demonstrating relevant skills
Publications at ACL, NeurIPS, or ICML conferences
Experience training models or evaluating model performance
Benefits
Competitive internship stipend.
Mentorship from experienced engineers.
Work on real, high-impact projects.
$1K monthly stipend for meals
Free Equinox membership
Team events and offsites.
Potential full-time return offer.
