Safety Engineer

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.

About the Role & Team

Putting an autonomous 40-ton excavator on a live jobsite means being able to say, precisely, what we know about its safety and what that knowledge rests on. Today that argument lives in hazard analyses, requirements, tests and logs, linked by hand.

As a Safety Engineering intern, you’ll explore how formal methods and LLM-assisted proving can strengthen that argument. Working at the intersection of formal methods, statistics, and field robotics, you’ll formalize a narrowly scoped safety claim, verify a checker in Lean, and evaluate it on real fleet data, with explicit assumptions and auditable results.

What You'll Do

• 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 We're Looking For

Required

• Hands-on experience with Lean and mathlib experience, particularly real analysis or probability.