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.
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.
WHAT WE’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.
Excitement 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 (e.g., eval frameworks, benchmark suites, failure-analysis reports or tooling) that demonstrate relevant skills.
BENEFITS
Health Benefits eligible at 30+ hours a week
Overtime eligible at 1.5x from 40 to 55 hours; 2x over 55 hours-week.
$1.5K monthly stipend for meals
Relocation & Housing Support
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
Mercor is an Equal Opportunity Employer and is committed to providing equal opportunity to all employees and applicants. Mercor does not discriminate based on actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression, sexual orientation, marital status, military service and veteran status, disability, protected medical condition as defined by California or local law, genetic information, or any other characteristic protected by applicable federal, California, or local laws and ordinances. Further, Mercor prohibits discrimination, harassment, and retaliation based on any combination of protected characteristics. This Policy applies to all areas of employment, including recruitment, hiring, training, promotion, compensation, benefits, discipline, termination, and all other privileges, terms, and conditions of employment. This Policy and the law prohibit employment discrimination against any employee or applicant based on any legally protected status.
