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Bedrock Robotics

Builds autonomy kits for construction equipment

Software Engineer - State Estimation & Localization

Software EngineeringUpdated on Nov 14
Full-timeSan Francisco, CA, USANo salary listed

About the Job

Requirements

  • At least 4 years of professional engineering or applied research experience in state estimation, localization, navigation, simultaneous localization and mapping (SLAM), or sensor fusion.
  • Strong foundations in probabilistic estimation, linear algebra, three-dimensional 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++; the production stack is primarily Rust, and experienced C++ engineers will receive support while ramping 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.

Responsibilities

  • 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.
  • Build metrics for fleet-wide monitoring of pose and calibration accuracy.
  • 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.

Desired Qualifications

  • 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, iterative closest point (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.

About the Company

Bedrock Robotics

Builds autonomy kits for construction equipment

Bedrock Robotics builds hardware and software that turn contractors' existing heavy equipment into autonomous machines for earthmoving on construction sites. Its Bedrock Operator kit combines sensors and onboard computing to perceive the surroundings, plan excavation, and move material alongside crews and other equipment. Site managers set the work plan, while the system carries out tasks and reports progress and performance. The company develops its technology with construction partners and equips their fleets with autonomy upgrades for large infrastructure and industrial projects. Bedrock Robotics was founded in 2024 and is headquartered in San Francisco, California.

Company size
51-200
Company stage
Series C
Total funding
$950M
Headquarters
San Francisco, CA
Founded
2024

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