Develop end-to-end machine-learning models for autonomous-driving taxis from camera and LiDAR data, then evaluate and optimize them in simulation and real vehicles.
About this role
Leads architecture and hands-on integration of an autonomous-driving vehicle system, connecting sensor and vehicle-state inputs to localization, model inference, planning and control. Defines component interfaces, data and timing conventions, latency limits, and allocation across vehicle computers and MCUs. The role selects and validates middleware and builds shared foundations for integration tests, log replay, simulation and repeatable vehicle checks, coordinating across specialist teams. Required experience is software design and implementation for robotics, automotive, embedded or other real-world systems, including leading component integration through hardware validation and defect analysis. Candidates need C/C++ system-software development and Linux debugging, ability to read and modify existing C/C++ and Python code, and design skills covering latency, processing cycles, concurrency, communication and compute limits. Deep expertise in at least one of sensing, localization, ML inference, planning or control, plus the ability to discuss design with adjacent specialists and align teams on technical trade-offs, is required.
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