AI Benchmarking Engineer — Evaluation & Failure Analysis

Doist; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Bi-annual bonus

Equity grant

Relocation bonus

Housing allowance

Meals stipend

Equinox membership

Laundry reimbursement

Wellness reimbursement

Healthcare

Job summary

Mercor in San Francisco is seeking a Research Engineer to join the intersection of engineering and applied AI research. You’ll own benchmarking pipelines, evaluation systems, and failure analysis workflows that inform training and improvement of frontier language models.

You’ll design and run evals, build rubrics and scorers, and collaborate with researchers, operators, and data teams to ensure data quality and scalable benchmarks in a fast-paced environment.

Qualifications

• Strong applied research background in model evaluation, benchmarking, and 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.

Responsibilities

• 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.

Job description

Mercor in San Francisco is seeking a Research Engineer to join the intersection of engineering and applied AI research. You’ll own benchmarking pipelines, evaluation systems, and failure analysis workflows that inform training and improvement of frontier language models.

You’ll design and run evals, build rubrics and scorers, and collaborate with researchers, operators, and data teams to ensure data quality and scalable benchmarks in a fast-paced environment.