Staff Software Engineer, Localization

AeroVect; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

AeroVect is seeking a Staff Software Engineer to join their Localization team in Greater London. This role involves taking ownership of the localization stack, ensuring production reliability on real hardware at active airports.

The ideal candidate has over 8 years of experience in robotics, strong experience with multi-sensor fusion, and proficiency in modern C++. A BS or MS in a related field is required. The position offers the chance to work on cutting-edge autonomous technology.

Qualifications

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

Responsibilities

• Take ownership of core areas of the localization stack.
• Contribute to the broader localization and mapping pipeline.
• Help design and build online validation and monitoring systems.

Skills

Localization stack ownership

Multi-sensor fusion

Non-linear optimization

Modern C++ (C++17+)

Linux and ROS/ROS2

Education

BS or MS in Computer Science or related field

Tools

Ceres

GTSAM

ROS/ROS2

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