The posting, in Mercor's own words
archived Oct 28, 2026About Mercor
Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
About the Role
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. Your work will define how we measure tool use, agentic behavior, and real-world reasoning. You’ll design and run evals, build rubrics and scorers, and turn failure analysis into actionable improvements for post-training, RLVR, and data pipelines.
Read the full posting ↓
What You’ll Do
Benchmarking: Design, implement, and maintain benchmarks and metrics for tool use, agentic behavior, and real-world reasoning; ensure benchmarks scale with training and stay aligned with product and research goals. 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 and compare runs at scale. Failure analysis: Run systematic failure analysis on model outputs (e.g., wrong tool use, reasoning errors, safety/alignment issues); categorize failure modes, quantify prevalence, and feed findings into reward design, data curation, and benchmark design. Rubrics and evaluators: Create and refine rubrics, automated evaluators, and scoring frameworks that drive training and evaluation decisions; balance rigor with scalability (human vs. model-as-judge, calibration, agreement). 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. Cross-team collaboration: Work with AI researchers, applied AI teams, and data producers to align evals with training objectives and to prioritize benchmarks and failure analyses that matter most. Ownership in a fast-paced environment: Operate in a high-iteration research setting with strong ownership of benchmarks, evals, and failure-analysis workflows.