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.

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.

Research Engineer Intern - Environments, Data and Post-Training

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Mercor

More roles at Mercor