Join the team bringing autonomy to the built world

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.

About the Role & Team

Before an autonomous excavator digs its first trench on a new site, it's already dug thousands in simulation. The Simulation team builds the virtual environments our autonomy stack trains and tests against — and the closer sim looks and behaves like the real world, the faster we move. Neural rendering is the next step: replacing hand-authored assets and approximations with learned representations that capture the visual complexity of real jobsites — dust, lighting, deformable terrain, heavy equipment in motion. As our Simulation intern, you'll push neural rendering techniques into our sim pipeline and measure whether they actually close the visual gap that matters for downstream autonomy performance.

What You'll Do

• Research, implement, and benchmark neural rendering methods (NeRF, 3D Gaussian splatting, or related techniques) for generating realistic construction-site imagery within our simulation stack
• Train models on real-world site data captured by our fleet and evaluate visual fidelity, temporal consistency, and render speed
• Integrate neural rendering outputs into the simulation pipeline so perception and planning teams can train and test against them
• Build evaluation metrics and tooling that quantify how well rendered scenes match real fleet data — and whether that improvement transfers to autonomy performance