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.
We are seeking a Head of Validation and Verification Engineering to lead validation, and verification. In this role, you will define the criteria for every unsupervised deployment and serve as the technical authority on readiness, participating in critical launch decisions. You will develop a validation architecture where adding a new machine or capability is an incremental, evidence-based process. Rather than inheriting old frameworks, you will leverage cutting-edge research and create new best practices for an emerging industry.
Our machines already operate unsupervised at customer sites. Everything ahead—introducing more machine types, tackling new tasks each quarter, expanding to new sites—pushes the boundaries of the safety standards that have brought us this far.
Safety validation of the system. Design the metrics, scenario libraries, and architectural coverage that quantify end-to-end behavioral safety — and make them hold up as the machine set and task set expand.
A validation architecture that generalizes. Decide what evidence is platform-independent and what must be re-established per machine, per task, per site. Turn the safety case into something composable instead of something rewritten.
Systems engineering. Own requirements decomposition and traceability from mission-level intent through subsystems, and use validation findings to expose the gaps, ambiguities, and conflicts that feed back into design.
V&V strategy and infrastructure. Choose the right instrument for each claim — log playback, closed-loop physics simulation, structured proving-ground testing, or on-site work — and specify the tooling and sim capability needed to make those instruments trustworthy across a growing fleet.
Hardware and functional safety. Validate the sensing, compute, and actuation kit we install on customer machines, including functional-safety readiness of safety-critical paths, as that kit is ported onto new platforms.
The org. Grow a team of software, systems, test, and hardware engineers plus data scientists, and scale it ahead of the machine count rather than behind it.
Decision-grade reporting. Define the KPIs — verification coverage, defect trends, release readiness, system maturity — and turn dense test data into the small number of charts that let leadership, customers, and insurers make a call.
A V&V framework and safety case structure that spans multiple machine types and an expanding task set, and that engineering, customers, and insurers all recognize as rigorous.
A scenario and coverage model built for the machines and tasks we have not shipped yet, not just the ones we have.
Readiness criteria that generalize — so the second platform, and the tenth, clear the bar faster than the first did without clearing a lower one.
A team that outlives any single launch.
BS or advanced degree in engineering, physics, or another technical field, or equivalent practical experience.
10+ years in test planning and experimental design for safety-critical autonomous or robotic systems, with a bias toward safety and repeatability.
Track record building and leading multidisciplinary technical organizations — software, systems, hardware, test, and data science under one roof.
Depth in sensing and compute: cameras, lidar, radar, GNSS, IMUs, edge compute, embedded controllers.
Fluency in data analysis and test automation (Python, C/C++) sufficient to interrogate results yourself, not only to review them.
Experience running many parallel test activities across non-colocated teams, sites, and stakeholders.
Judgment about when a claim is proven and when it merely looks proven.
You have taken a driverless or unsupervised system through to real deployment and personally held the readiness gate — and then had to do it again for a second platform.
You have scaled a validation program across a heterogeneous fleet, multiple sites, or successive hardware generations.
You have authored or led a formal safety case, or contributed to safety standards work (UL 4600, ISO 26262, ISO 21448, or off-road equivalents).
Functional safety experience on vertically integrated or custom hardware.
Familiarity with heavy construction machines — excavators, articulated dump trucks, dozers, wheel loaders, motor graders.
A record of external technical contribution: publications, keynotes, standards bodies, or research leadership. We want this role visible in the field, and we will support that.
We are a startup, so we look for engineers with an appetite for agility. We do not compromise on safety and we still find ways to move extremely fast — the entire point of this role is that those two things are compatible when the evidence is good enough.
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.
Lead the Americas Observability Architects practice, setting regional strategy and developing teams that drive customer onboarding, adoption, retention, and expansion while shaping product direction and technical customer success.
Leads enterprise AI transformation by setting a prioritized roadmap, scaling successful initiatives, and embedding responsible AI practices that deliver measurable business outcomes across functions.
Leads enterprise AI transformation by setting a prioritized roadmap, scaling successful initiatives, and embedding responsible AI into business processes to deliver measurable outcomes across the organization.
Leads enterprise AI transformation by setting a prioritized roadmap, scaling successful initiatives, and embedding responsible AI into business processes to deliver measurable outcomes across the organization.
Leads enterprise AI transformation by shaping a prioritized roadmap, scaling successful initiatives, and embedding responsible AI practices that deliver measurable business outcomes across functions.
