About this role
Join the team bringing advanced autonomy to the built worldAt 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.
About the Role & TeamConstruction 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.
What You'll DoPrototype 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
What We're Looking ForRequiredPursuing 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
PreferredResearch 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
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.