At least 5 years of experience shipping or operating perception systems for robotics, AV, or ADAS, with experience debugging and improving real-world performance.
Experience working in a research setting, independently driving research end-to-end, gathering data, and presenting findings to peers, including demonstrated leadership guiding a small team to a state-of-the-art result.
Strong knowledge across machine learning, perception, prediction, planning, and simulation, including experience with end-to-end, purely image-based algorithms.
Deep fundamentals in geometry and multi-sensor systems, including practical calibration know-how and the ability to diagnose alignment or time-synchronisation issues.
Proficiency in C++ and Python, with the ability to write production-quality code and build lightweight tools or checks.
Experience navigating noisy, production-like environments, including log-driven root-cause analysis across software, configuration, and sensor behaviour.
Engineering discipline, including reproducible workflows, clear metrics, and validation-minded execution.
Demonstrated proficiency in leveraging AI tools and systems to drive efficiency and innovative problem-solving.
Preferred Qualifications
Familiarity with perception model lifecycle elements such as dataset curation, evaluation metrics, regression detection, deployment constraints, and inference performance optimization.
3+ years of experience with common robotics/perception libraries such as OpenCV, PCL, and Ceres, and tools for visualisation and debugging.
Experience with sensor fusion, tracking, or 3D perception pipelines, and interest in growing deeper in these areas over time.