- Robot type
- Autonomous Vehicle · Defense
- Location
- SeattleWashingtonUSA
- Job type
- Software
- Posted
- Jul 10, 2026
Full-time
Technical Lead - State Estimation
Job description
In this role, you'll lead the architecture and development of our state estimation stack, solving challenging problems across localization, mapping, sensor fusion, and probabilistic inference. You'll work at the intersection of cutting-edge robotics research and production autonomy, turning advanced estimation techniques into robust systems that perform reliably in real-world deployment.
As a technical leader, you'll set the direction for state estimation at Overland, mentor a team of exceptional robotics engineers, and collaborate across perception, planning, controls, and platform engineering to build the next generation of autonomous off-road vehicles.
Job responsibilities
- Design and implement odometry, localization, and mapping algorithms that enable reliable autonomy in GPS-denied and degraded environments.
- Develop robust multi-sensor fusion systems combining IMUs, LiDAR, cameras, GNSS, wheel encoders, and other onboard sensors.
- Formulate and solve estimation problems using Kalman filtering, Bayesian inference, factor graphs, nonlinear optimization, and modern probabilistic techniques.
- Evaluate and integrate learned approaches—including learned odometry, feature representations, and neural mapping methods—where they deliver measurable improvements over classical techniques.
- Develop high-performance, production-quality C++ (C++23) software optimized for real-time robotic systems.
- Build tooling, simulation infrastructure, and evaluation pipelines that enable rapid algorithm development and validation using large-scale field datasets.
- Lead verification and validation efforts across diverse terrain, weather conditions, and operational environments.
- Partner closely with perception, planning, controls, and systems engineers to deliver an integrated, reliable autonomy stack.
Job requirements
- MS or PhD in Robotics, Computer Science, Electrical Engineering, or a related technical field with specialization in state estimation, SLAM, localization, or probabilistic inference.
- 5+ years developing production-grade state estimation or SLAM systems deployed on physical robotic platforms.
- Deep expertise in probability theory, Bayesian estimation, optimization, and nonlinear inference, including:
- EKF, UKF, Error-State Kalman Filters
- Factor graph optimization (GTSAM, Ceres, g2o)
- MAP/MLE estimation
- Demonstrated experience deploying robust estimation systems in complex, unstructured, or off-road environments.
- Expert-level C++ and strong Python development skills.