Research Engineer Intern - Environments, Data and Post-Training

Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise.

We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows.

Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence.

Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London.

About the Role

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

Your work will be used to train large language models to master tool use, agentic behavior, and real-world reasoning in real-world production environments. You’ll shape rewards, run post-training experiments, and build scalable systems that improve model performance. You’ll help design and evaluate datasets, create scalable data augmentation pipelines, and build rubrics and evaluators that push the boundaries of what LLMs can learn.

What You’ll 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 (e.g., GRPO, DAPO) 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.

What We’re Looking For

• 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