Part-time student role supporting AI validation and benchmarking for autonomous vehicle systems. Responsibilities include running benchmark pipelines, building ground truth datasets, developing evaluation metrics, analyzing failures, and automating tooling. Requires current enrollment in CS/Engineering program, 20+ hrs/week on-site, and familiarity with Python and ML evaluation concepts.
Salary not listed
HybridEntry levelML Engineering
About the role
Responsibilities
Run and maintain the benchmark pipeline, analyzing results to identify routing errors and regressions across agent variants
Build and expand ground truth datasets used to evaluate agent outputs against known-correct answers
Identify and address gaps in benchmark validation and support building a more comprehensive evaluation infrastructure to improve validation prior to release
Develop new evaluation dimensions such as label accuracy and structured output correctness beyond the existing team classification benchmarks
Investigate failure modes in agent outputs and work with engineers to surface actionable improvements
Write scripts and tooling to automate data collection, result parsing, and metric reporting
Document findings, track benchmark trends over time, and present results to the team
Requirements
Currently enrolled in a B.S. or M.S. in Computer Science, Data Science, Engineering or a related field
Available to commit to a minimum three-month assignment
Able to commit to a minimum of 20 hours per week
Able to work on-site at one of our office locations
Familiar with Cursor or Claude
Familiar with Python
Familiar with evaluation concepts: precision, recall, F1 score, and confusion matrices
Comfortable working with structured data (CSV, JSON)
Experience modifying or writing reproducible analysis scripts
Nice-to-Haves
Prior exposure to LLM-based systems, prompt engineering, or AI agent evaluation
Experience with Jira or Slack (e.g. ticketing systems, messaging apps)
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