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Bedrock Robotics is deploying autonomous systems on heavy construction equipment across the country. The company has raised $350M in two years and achieved the first fully autonomous excavator deployments in construction, with a team that includes veterans from Waymo, Segment, and Uber Freight.
You will develop state-estimation systems that allow autonomous machines to understand their position, orientation, motion, and configuration in real-world construction environments. This role combines data from GNSS, IMUs, lidar, machine sensors, and other sources to enable machines to operate safely and precisely in rugged, dynamic job-site conditions.
Key responsibilities include:
- Design, implement, and deploy state-estimation and localization algorithms for autonomous construction machines
- Combine GNSS, IMU, lidar, and machine-sensor data into accurate, real-time estimates of machine position, motion, and configuration
- Improve reliability through sensor monitoring, consistency checks, fault detection, trustworthy confidence estimates, redundancy, and graceful degradation
- Handle measurements that arrive late, out of order, intermittently, or not at all
- Build ground-truth systems, offline reference estimators, metrics, and regression tests
- Work on sensor calibration, clock synchronization, lidar-based arm estimation, and joint offline estimation
- Diagnose issues using field data, recorded-data replay, and simulation, then test improvements on physical machines
- Collaborate with controls, perception, safety, hardware, and systems teams
This is a hands-on role spanning algorithm design, production software, data analysis, and testing on real machines. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations cannot address.
REQUIREMENTS:
- 4+ years of professional engineering or applied research experience in state estimation, localization, navigation, SLAM, or sensor fusion
- Strong foundations in probabilistic estimation, linear algebra, 3D geometry, and numerical methods
- Hands-on experience with one or more of: GNSS/INS fusion, Kalman filtering, factor graphs, lidar or visual odometry, point-cloud registration, or sensor calibration
- Strong production software skills in Rust or modern C++. Production stack is primarily Rust; experienced C++ engineers supported during ramp-up
- Experience measuring estimator performance using ground truth, recorded data, simulation, and real-world testing
- Strong debugging and data-analysis skills across algorithms, software, sensors, and hardware
- Degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Applied Mathematics, or related field, or equivalent practical experience
DESIRABLE EXPERIENCE:
- Deploying state-estimation systems on autonomous vehicles, robots, or embedded platforms
- Estimator monitoring, uncertainty, fault detection, redundancy, or safety-relevant systems
- Nonlinear optimization, factor graphs, or smoothing
- Deep knowledge of GNSS, inertial sensing, lidar, sensor timing, calibration, and real-world failure modes
- Lidar localization, ICP, continuous-time estimation, or articulated-machine estimation
- Building offline reference estimators or independent ground-truth systems
- Production experience with Rust
- Familiarity with construction, mining, agricultural, or heavy industrial machines
