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Bedrock Robotics

2027 Internship Evaluation Engineer, Metric Prototyping

San Francisco, CAinterninternAdded today

About this role

Join Bedrock's Evaluation team as an intern to design and prototype metrics that measure autonomous construction equipment performance. You'll work with real fleet data and field observations to build evaluation frameworks that validate autonomy improvements before deployment on live job sites.

What you'll do

  • Prototype new metrics and benchmarks for autonomous construction tasks in collaboration with ML and data engineers
  • Extract key reference examples and edge cases from fleet datasets to test metric formulations
  • Conduct field observations at test sites to ground metrics in real-world machine performance
  • Collect ground-truth measurements on job sites to validate quantitative concepts against actual results
  • Build evaluation tools to identify performance regressions and design feedback loops for analysis acceleration
  • Document metric definitions, assumptions, and failure modes for team maintainability

What they're looking for

  • Python programming and data pipeline development
  • Statistical analysis and experimental design
  • Metric definition and benchmarking methodology
  • Technical communication and documentation
  • Geospatial data and point cloud processing
  • Autonomous vehicle or robotics evaluation experience
  • Problem translation from qualitative to quantitative domains
  • Field data collection and validation
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Bedrock Robotics

Bedrock Robotics develops autonomous construction machinery powered by AI and robotics technology. The company is hiring for roles spanning developer infrastructure, simulation systems, hardware engineering, field robotics application, and frontend engineering to support the development and deployment of autonomous excavators and heavy equipment.

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Likely interview questions

  • Walk us through how you'd translate a vague construction requirement like 'good grading' into a concrete, measurable metric with clear success criteria.
  • Describe your experience working with real-world datasets — what challenges did you face in bridging simulation and physical validation?
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