Research Engineer Intern Jobs in San Francisco

About Mercor

Mercor's mission is to organize human intelligence to power the AI economy. It is a leading AI data company that builds 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 how AI affects professional work in the real world. Mercor Enterprise brings the same infrastructure to Fortune 500 companies, helping them capture how their best people work and turning that expertise into agents.

Mercor is creating a new category of work where expertise powers AI advancement. The team is ambitious, fast-paced and deeply committed. You will work alongside researchers, operators and AI companies shaping systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. The team works in person five days a week in its San Francisco, NYC or London offices.

About the Role

As a Research Engineer at Mercor, you will work at the intersection of engineering and applied AI research. You will own benchmarking pipelines, evaluation systems and failure analysis workflows that directly inform how frontier language models are trained and improved.

Your work will define how tool use, agentic behavior and real-world reasoning are measured. You will design and run evals, build rubrics and scorers, and turn failure analysis into actionable improvements for post-training, RLVR and data pipelines.

What You Will Do

• Benchmarking: Design, implement and maintain benchmarks and metrics for tool use, agentic behavior and real-world reasoning. Make sure benchmarks scale with training and stay aligned with product and research goals.
• Evaluation systems: Build and operate end-to-end LLM evaluation systems, including 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 (for example, wrong tool use, reasoning errors and safety or alignment issues). Categorize failure modes, quantify how often they occur, 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 versus model-as-judge, calibration and agreement).
• Data quality and usability: Measure 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 prioritize the benchmarks and failure analyses that matter most.
• Ownership in a fast-paced environment: Work in a high-iteration research setting with strong ownership of benchmarks, evals and failure-analysis workflows.

What We Are Looking For

• Strong applied research background, with a 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.
• Willingness to work in person in San Francisco five days a week in a high-intensity, high-ownership environment.

Nice to Have

• Industry experience on a post-training or evaluation/benchmarking team (highest priority).
• Publications at top-tier venues (NeurIPS, ICML, ACL), especially in evaluation or benchmarking.
• Experience building or running LLM evaluations, benchmarks or failure-analysis pipelines.
• Experience with synthetic data generation, rubric design or RL-style workflows that use evals for reward shaping.
• Work samples or code (for example, eval frameworks, benchmark suites, failure-analysis reports or tooling) that demonstrate relevant skills.

Benefits

• $1.5K monthly stipend for meals
• Free Equinox membership
• $200 monthly laundry reimbursement
• $200 monthly personal wellness reimbursement