Research Engineer Intern - Environments, Data and Post-Training

Mercor•San Francisco, CA

2d•$180,000 - $500,000•Onsite

About The Position

Mercor is hiring a Research Engineer Intern to work at the intersection of engineering and applied AI research. The role contributes to post-training and RLVR, synthetic data generation, and large-scale evaluation workflows that affect frontier language models. The work supports training models to develop tool use, agentic behavior, and real-world reasoning in production environments. The intern will shape rewards, run post-training experiments, and build scalable systems to improve model performance. They will help design and evaluate datasets, develop scalable data augmentation pipelines, and create rubrics and evaluators to guide model learning and evaluation. The role involves close collaboration with AI researchers, applied AI teams, and experts producing training data in a fast-paced, experimental environment with rapid iteration and high ownership. Mercor works in person five days a week in its San Francisco, NYC, or London offices. This role's requirements specify in-person work in San Francisco five days a week, with optional remote Saturdays.

Requirements

Strong applied research background focused 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.
Willingness 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 Haves

Real-world post-training team experience in industry (highest priority).
Publications at top-tier conferences such as NeurIPS, ICML, or 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.

Responsibilities

Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies affect model performance.
Design and run reward-shaping experiments and algorithmic improvements, such as GRPO and 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 to guide training and evaluation decisions.
Build and operate LLM evaluation systems, benchmarks, and metrics at scale.
Collaborate with AI researchers, applied AI teams, and experts producing training data.
Work in a fast-paced experimental research environment with rapid iteration cycles and high ownership.