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