2027 Internship Safety Engineer, Agentic Safety Case Assessment

Bedrock Robotics • San Francisco, CA

43d

About The Position

Join the team bringing advanced autonomy to the built world. At Bedrock, we're moving AI out of the lab and into the real world. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects. We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction. This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us.

Requirements

• Hands-on experience with Lean and mathlib experience, particularly real analysis or probability.
• Working knowledge of probability and statistics (distributions, confidence intervals, tail bounds).
• Familiarity with knowledge graphs or structured document analysis
• Ability to translate informal claims into precise specifications and identify their assumptions
• Experience evaluating LLM-generated code or proofs.
• Background in autonomous vehicles, heavy equipment, or other safety-critical robotics.

Nice To Haves

• Exposure to safety or assurance-case practice (GSN, UL 4600, ISO 13849, IEC 61508) or runtime verification.
• Experience with Rust verification tools (Aeneas, Verus, Kani) or differential/property-based testing.
• Familiarity with knowledge graphs or structured document analysis
• Background in autonomous vehicles, heavy equipment, or other safety-critical robotics

Responsibilities

• Become fluent in Bedrock's safety case and trace safety claims through the hazards, requirements, mitigations, and evidence that support them
• Build an end-to-end demonstration for one claim: define the property and assumptions, implement a checker in Lean, prove its correctness against the specification, and run it on fleet data
• Develop checks for assumptions that can be monitored in fleet data, and document those that require other evidence
• Build audit tooling for LLM-assisted proving: record proof dependencies, document toolchain trust assumptions, and reject proof placeholders or unapproved axioms in automated builds
• Work with Safety, Systems, and Autonomy teams to review formal specifications and turn findings into safety case improvements
• Document the results, limitations, and recommendations for adoption in our review process

What This Job Offers

• Career Level: Intern
• Education Level: No Education Listed