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Software Engineer, Localization, Calibration & Mapping

AeroVect
Canada+4Remote
Employment
Full-time

About the role

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 .

We are looking for a Software Engineer II to join the Localization team. Your initial focus will be validation and metrics - extending our regression suite, building performance monitoring, and ensuring localization quality as we scale to new environments and hardware platforms. Over time, you will grow into broader localization engineering work. You will report to a Staff Localization Engineer and collaborate with the Infrastructure, Sim, and Safety teams.

You Will

  • Extend and maintain our localization regression suite and CI-based validation pipeline

  • Build metrics and analyzers that catch performance degradation before it reaches production

  • Develop localization health monitoring and diagnostics tooling

  • Work with real sensor data from autonomous operations to validate and debug localization performance

  • Support the team on general localization tasks - sensor integration, performance tuning, platform migrations - with increasing scope over time

You Have

  • 2+ years of experience in robotics or autonomous vehicles

  • Strong Python/C++ skills

  • Experience with Linux and ROS/ROS2

  • Hands-on experience building test or validation infrastructure - replay pipelines, automated metrics, regression frameworks

  • Understanding of localization fundamentals (sensor fusion, SLAM, GNSS/INS)

  • BS in Computer Science, Robotics, Electrical/Mechanical Engineering, or related field

We Prefer

  • MS with focus on localization, state estimation, or robotics

  • Experience with CI/CD pipelines for offline evaluation at scale

  • Familiarity with graph-based SLAM or optimization tools (GTSAM, Ceres, GLIM)

  • Exposure to embedded compute platforms (Jetson or similar)

  • Experience with safety-critical validation or V&V processes

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