Research Engineer – Benchmarking

Job summary

Responsibilities

As a Research Engineer at Mercor, you’ll work at the intersection of engineering and applied AI research. You’ll own benchmarking pipelines, evaluation systems, and failure analysis workflows that directly inform how we train and improve frontier language models.

• Benchmarking: Design, implement, and maintain benchmarks and metrics for tool use, agentic behavior, and real-world reasoning; ensure benchmarks scale with training and stay…
• Evaluation systems: Build and operate LLM evaluation systems end-to-end runs, scoring, dashboards, and reporting, so researchers and applied AI teams can track model performance…
• Failure analysis: Run systematic failure analysis on model outputs (e.g., wrong tool use, reasoning errors, safety/alignment issues); categorize failure modes, quantify…
• Rubrics and evaluators: Create and refine rubrics, automated evaluators, and scoring frameworks that drive training and evaluation decisions; balance rigor with scalability…
• Data quality and usability: Quantify data usability, quality, and impact on key benchmarks; use evals and failure analysis to guide data generation, augmentation, and curation.

What they're looking for

• Strong applied research background, with focus on model evaluation, benchmarking, and/or failure analysis.
• Strong coding skills and hands-on experience with ML models and evaluation code.
• Solid grasp of data structures, algorithms, and backend systems.
• Comfort with APIs, SQL/NoSQL, and cloud platforms for running and storing eval results.
• Ability to reason about model behavior, experimental results, and data quality from evals and failure analyses.

Tech stack

• SQL