Technical Lead - State Estimation

Key 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.
• Create 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.
• Mentor engineers, drive technical excellence, and establish best practices for robotics software development.

Minimum Qualifications

• 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
• Expert-level C++ and strong Python development skills.
• Experience building low-latency, real-time robotics software.
• Strong understanding of inertial navigation, sensor calibration, and multi-modal sensor fusion (IMU, LiDAR, cameras, GNSS).
• Demonstrated experience deploying robust estimation systems in complex, unstructured, or off-road environments.

Desired Qualifications & Experience

• PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
• Experience incorporating machine learning into estimation pipelines.
• Experience with ROS 2 and real-time middleware (DDS, shared-memory transport).
• Experience with terrain-relative navigation, prior-map localization, or GPS-denied navigation.
• Contributions to widely used open-source robotics or estimation libraries.
• Experience leading technical teams, mentoring engineers, and driving architecture decisions.
• Experience shipping autonomy systems on production robotic or autonomous vehicle platforms.

Benefits

• Salary range: $200K to $260K annually
• Equity compensation
• Best-in-class healthcare, dental and vision plans
• Flexible PTO
• 401k with company match
• Parental leave