Staff Software Engineer, Localization

AeroVect; Toronto; On-site; CAD 100,000 - 130,000; Full time

Job summary

AeroVect in Toronto is seeking a Staff Software Engineer to enhance its localization team. You will lead significant parts of the localization, mapping, and calibration stack for autonomous ground operations in major airports.

The ideal candidate will have over 8 years of experience in robotics, strong skills in multi-sensor fusion, and a proven track record in deploying localization systems. Responsibilities include developing tooling for diagnostics and contributing to the broader localization pipeline.

Qualifications

• 8+ years in robotics or autonomous vehicles with experience in localization.
• Deep grounding in multi-sensor fusion and state estimation.
• Strong command of non-linear optimization and filtering techniques.

Responsibilities

• Take ownership of core areas of the localization stack.
• Contribute across the localization and mapping pipeline.
• Develop tooling for diagnostics and health logging.

Skills

Multi-sensor fusion

State estimation

Technical leadership

C++ (C++17+)

Linux

ROS/ROS2

Non-linear optimization

Education

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

Tools

Ceres

GTSAM

g2o

Job description

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 elevate 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