2027 Internship State Estimation, Learned Mapping & Semantic SLAM

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

Construction sites change with every bucket of dirt. Our State Estimation team builds the maps and localization systems that help autonomous excavators understand where they are and how the terrain is changing.

As an intern on this team, you'll explore how modern learning-based methods can improve our geometry-first mapping stack. That could mean localizing reliably in terrain that looks the same in every direction, building maps that hold up through dust and occlusion, or labeling the map semantically so the machine can tell material to dig from haul roads, spoil piles, and berms. You'll test your ideas on real fleet data, measure them against strong classical baselines, and deliver a prototype the team can build on.

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 ."

Responsibilities

Prototype learned SLAM and mapping methods, such as place recognition, odometry, depth completion, and neural occupancy or surface representations; Fuse lidar and camera segmentation into consistent 3D semantic maps, potentially using vision foundation models or open-vocabulary segmentation; Develop methods that handle changing terrain, moving material, sparse returns, dust, occlusion, and perceptual aliasing; Train models on fleet lidar and camera data, and build evaluation pipelines to compare mapping and localization performance against existing methods and ground truth; Work with perception and planning teams to identify the map properties that matter most for downstream decisions; Deliver a documented prototype, experimental results, and recommendations for future work

Requirements

Pursuing a BS, MS, or PhD in computer science, robotics, electrical engineering, or a related field, or equivalent research or industry experience; Strong Python skills and hands-on model training experience with PyTorch or a similar framework; Solid understanding of 3D geometry, coordinate frames, and transforms; Familiarity with SLAM and mapping fundamentals, point clouds, or depth data; Comfort with messy sensor data and designing experiments that distinguish real improvements from noise

Nice to have

Research or project experience in learned SLAM, semantic mapping, or 3D scene understanding; Experience with neural scene representations, such as NeRFs, 3D Gaussian splatting, neural occupancy, or signed distance fields; Experience applying vision foundation models, such as DINOv2 or SAM, to 3D or robotics problems; Experience with lidar processing or multi-sensor fusion; Exposure to autonomous vehicle, off-road, or field robotics data; Familiarity with Rust or C++, and ROS or similar robotics middleware