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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.
Job Responsibility:
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
Requirements:
Pursuing a degree in Computer Science or a related field (graduating 2025–2027)
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