Staff Software Engineer, Localization
Who We Are
AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.
We are looking for a Staff Software Engineer to join the Localization team and own significant parts of our localization, mapping, and calibration stack as we scale autonomous ground operations across major airports. You'll take deep ownership of specific workstreams, contribute to the team's technical direction, and partner across the perception, planning, and platform interfaces to help drive the autonomous system from supervised operation to driverless. You'll work alongside the team's senior localization engineers.
You Will
Take ownership of one or more core areas of the localization stack (LiDAR-inertial-GNSS state estimation, 3D mapping and map maintenance, or sensor calibration) and drive it to production reliability on real hardware at active airports
Contribute across the broader localization and mapping pipeline from sensor integration, performance tuning, regression testing, and deployment to new operating environments
Help design and build online validation that monitors localization integrity and cross-sensor consistency during live missions, detects drift, and integrates with the vehicle's safety architecture
Develop tooling for diagnostics, health logging, and post-mission analysis across the stack
Contribute to the regression, validation, and release-gating approach for localization changes deployed to active airports
Deploy, test, and iterate using data from real autonomous operations
You Have
8+ years in robotics or autonomous vehicles, with a track record of owning localization or state-estimation systems through production deployment on real hardware
Deep practical grounding in multi-sensor fusion and state estimation across LiDAR, IMU, GNSS, and cameras
Demonstrated technical leadership - driving architecture across teams, setting direction, and being the person others escalate to on hard estimation problems
Strong command of non-linear optimization (Ceres, GTSAM, g2o) and/or filtering (EKF, UKF), with the judgment to know when each applies
Strong modern C++ (C++17+) and deep working experience with Linux and ROS/ROS2
Understanding of how calibration quality propagates through localization and perception, and how localization errors propagate into the safety case
BS or MS in Computer Science, Robotics, Electrical/Mechanical Engineering, or a related field
We Prefer
MS or PhD with a focus on localization, state estimation, or calibration
Hands-on experience with multi-sensor calibration - intrinsic, extrinsic, and temporal
Experience with factor graphs, graph-based SLAM, or open-source tools like GLIM, LIO-SAM, Cartographer, Kalibr, Ceres, GTSAM
Experience building online/runtime monitoring and defining safety-relevant thresholds within a safety monitoring architecture
Track record of taking an autonomous system toward driverless operation
AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.
We are looking for a Staff Software Engineer to join the Localization team and own significant parts of our localization, mapping, and calibration stack as we scale autonomous ground operations across major airports. You'll take deep ownership of specific workstreams, contribute to the team's technical direction, and partner across the perception, planning, and platform interfaces to help drive the autonomous system from supervised operation to driverless. You'll work alongside the team's senior localization engineers.
You Will
Take ownership of one or more core areas of the localization stack (LiDAR-inertial-GNSS state estimation, 3D mapping and map maintenance, or sensor calibration) and drive it to production reliability on real hardware at active airports
Contribute across the broader localization and mapping pipeline from sensor integration, performance tuning, regression testing, and deployment to new operating environments
Help design and build online validation that monitors localization integrity and cross-sensor consistency during live missions, detects drift, and integrates with the vehicle's safety architecture
Develop tooling for diagnostics, health logging, and post-mission analysis across the stack
Contribute to the regression, validation, and release-gating approach for localization changes deployed to active airports
Deploy, test, and iterate using data from real autonomous operations
You Have
8+ years in robotics or autonomous vehicles, with a track record of owning localization or state-estimation systems through production deployment on real hardware
Deep practical grounding in multi-sensor fusion and state estimation across LiDAR, IMU, GNSS, and cameras
Demonstrated technical leadership - driving architecture across teams, setting direction, and being the person others escalate to on hard estimation problems
Strong command of non-linear optimization (Ceres, GTSAM, g2o) and/or filtering (EKF, UKF), with the judgment to know when each applies
Strong modern C++ (C++17+) and deep working experience with Linux and ROS/ROS2
Understanding of how calibration quality propagates through localization and perception, and how localization errors propagate into the safety case
BS or MS in Computer Science, Robotics, Electrical/Mechanical Engineering, or a related field
We Prefer
MS or PhD with a focus on localization, state estimation, or calibration
Hands-on experience with multi-sensor calibration - intrinsic, extrinsic, and temporal
Experience with factor graphs, graph-based SLAM, or open-source tools like GLIM, LIO-SAM, Cartographer, Kalibr, Ceres, GTSAM
Experience building online/runtime monitoring and defining safety-relevant thresholds within a safety monitoring architecture
Track record of taking an autonomous system toward driverless operation