2027 Internship State Estimation, Learned Mapping & Semantic SLAM
Bedrock Robotics Inc4 days ago
Responsibilities
- Prototype learned SLAM and mapping methods, including place recognition, odometry, depth completion, and neural occupancy or surface representations.
- Fuse lidar and camera segmentation into consistent 3D semantic maps.
- Develop methods for changing terrain, moving material, sparse returns, dust, occlusion, and perceptual aliasing.
- Train models on fleet lidar and camera data and build evaluation pipelines for mapping and localization performance.
- Collaborate with perception and planning teams to identify important map properties 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.
- Ability to work with messy sensor data and design experiments that distinguish real improvements from noise.
- Preferred: research or project experience in learned SLAM, semantic mapping, or 3D scene understanding.
- Preferred: experience with neural scene representations such as NeRFs, 3D Gaussian splatting, neural occupancy, or signed distance fields.
- Preferred: experience applying vision foundation models such as DINOv2 or SAM to 3D or robotics problems.
- Preferred: experience with lidar processing or multi-sensor fusion.
- Preferred: exposure to autonomous vehicle, off-road, or field robotics data.
- Preferred: familiarity with Rust or C++, and ROS or similar robotics middleware.
Categories
ML EngineeringRobotics
About Bedrock Robotics Inc
Bedrock Robotics builds autonomous control systems that retrofit heavy construction equipment, enabling driverless operation on large infrastructure and industrial projects. The San Francisco–based, privately held company, founded in 2024, deploys its technology with contractors and project owners to speed schedules and improve job-site safety. Its business centers on upgrading existing fleets and operating them in the field, with software, sensors, and integration services tailored to construction workflows.