Research Engineer Intern

Mercor•San Francisco, CA

6d•Onsite

About The Position

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.

Requirements

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 Haves

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

Responsibilities

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.