At Bedrock, we're moving AI out of the lab and onto real job sites. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. We retrofit the heavy equipment contractors already own, starting with excavators, so experienced crews can safely take on more projects and deliver more of the critical infrastructure our society needs.
In August 2026, Bedrock-equipped excavators began doing fully autonomous, paid work on live commercial job sites. We've more than doubled our customer base since 2025, and our machines now work with contractors across the South, Midwest, and Mid-Atlantic
In October 2026, we raised a $600M Series C co-led by CapitalG and Emergence Capital, bringing our total funding to nearly $1B. We're using it to put more autonomous machines to work, build the field and operations teams behind every deployment, and expand into new machine types and job-site tasks.
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.
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
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.
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
About This Internship
This is a fixed-term, project-based internship designed strictly for students (undergraduate or graduate) seeking resume-building experience, hands-on skill development, or industry exposure rather than a direct career launchpad. Candidates will own discrete, short-term projects that can be fully completed within the internship timeframe. Please note that while exceptional interns may occasionally be considered if business needs align, opportunities for full-time conversion are highly limited."
Bedrock Robotics is an Equal Opportunity Employer
We’re committed to building a diverse and inclusive workplace. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, veteran status, genetic information, or any other protected characteristic.
Reasonable Accommodations
We want our hiring process to be accessible to everyone. If you need an accommodation to participate in the application or interview process, please let your recruiter know so we can support you.