Software Engineer, State Estimation & Localization

Join the Team Bringing Advanced Autonomy to the Built World

At 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.

We’re looking for a Software Engineer to develop the state-estimation systems that allow autonomous machines to understand their position, orientation, motion, and configuration. You’ll build production software that combines data from GNSS, IMUs, lidar, machine sensors, and other sources in rugged environments the machines themselves are actively reshaping.

Accurate state estimation is essential to both safety and performance. Your work will help our machines operate precisely, recognize when sensor data or estimates are unreliable, and respond safely when inputs are delayed, degraded, or unavailable.

Depending on your background, you may focus on real-time sensor fusion, estimator reliability, lidar-based estimation, offline optimization, or calibration. This is a hands-on role spanning algorithm design, production software, data analysis, and testing on real machines.

What You’ll Do

• 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 that measure performance and expose failures
• Work on related problems such as 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 to improve the performance of the complete autonomy system

What We're Looking for

• 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++. Our production stack is primarily Rust, and we will support experienced C++ engineers as they ramp up
• Experience measuring estimator performance using ground truth, recorded data, simulation, and real-world testing
• Strong debugging and data-analysis skills, including the ability to investigate problems across algorithms, software, sensors, and hardware
• A degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Applied Mathematics, or a related field, or equivalent practical experience

Ways to Stand Out

• Experience deploying state-estimation systems on autonomous vehicles, robots, or embedded platforms
• Experience with estimator monitoring, uncertainty, fault detection, redundancy, or safety-relevant systems
• Experience with nonlinear optimization, factor graphs, or smoothing
• Deep knowledge of GNSS, inertial sensing, lidar, sensor timing, calibration, and real-world failure modes
• Experience with lidar localization, ICP, continuous-time estimation, or articulated-machine estimation
• Experience building offline reference estimators or independent ground-truth systems
• Production experience with Rust
• Familiarity with construction, mining, agricultural, or other heavy industrial machines

Other Special Aspects of the Role

• Based in San Francisco with the ability to be onsite at our SF office 3 day a week