2027 Internship Safety Engineer, Agentic Safety Case Assessment

2 нед. назад

СШАTraineeOnsite

formal methodsstatisticslean

Internship role focused on safety engineering for autonomous construction equipment.

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.
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

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

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.
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.