Key Notes
Practical localisation expertise combining GNSS, IMU and wheel odometry is required for this full-time, hybrid London role. As a senior individual contributor, you will define onboard architecture and deploy production algorithms. Modern C++ and Python are essential; camera, LiDAR or radar fusion, embedded deployment and relevant postgraduate study are desirable.
What You'll Work On
- Define localisation architecture and develop state-estimation algorithms combining GNSS, IMU and wheel odometry, with scope to incorporate additional sensor measurements.
- Deploy localisation software on real-time vehicle platforms, implementing fault handling and degraded modes while coordinating integration across controls, sensors and embedded teams.
- Build validation datasets, accuracy metrics and regression tests, comparing ground truth and investigating failures while mentoring engineers across the state-estimation stack.
Why This Role Matters
- Fault handling and degraded modes help localisation software provide usable motion and pose estimates when sensor conditions change, supporting the onboard systems that consume those estimates.
- Ground-truth comparisons expose localisation errors that aggregate metrics may obscure, supporting evidence-based decisions about algorithm changes and regression risk across different vehicle platforms.
What They Are Looking For
- Technical: Relevant technical capabilities include GNSS/IMU/odometry fusion, EKF/UKF or factor-graph methods.
- Experience: Relevant experience includes production real-time localisation and rigorous benchmarking or failure analysis.
- Technical: Relevant technical capabilities include modern C++ and Python, alongside hands-on architecture leadership.
- Preferred: Preferred attributes include GTSAM/Ceres/g2o, MISRA C++, embedded systems or relevant MS/PhD/equivalent experience.