Research Engineer - Environments, Data and Post-Training

Job summary

Responsibilities

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.

• 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.

What they'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.

Tech stack

• SQL