Adapt and tune Context Fusion modules across different vehicle platforms, sensor configurations, map providers, and regional driving environments, ensuring robust performance in real-world scenarios.
Conduct on-vehicle validation, debugging, and performance tuning, including real-world issue reproduction and iteration on fixes.
Design and develop testing tools, automation pipelines, and quality metrics to improve triage efficiency, enable cross-carline comparison, and scale engineering productivity.
Evaluate fusion outputs and their impact on downstream planning, prediction, and active safety; identify gaps and propose practical solutions for production programs.
Travel domestically and internationally for on-site vehicle testing and OEM collaboration as needed.
What We Need To See
BS/MS, or equivalent experience in Computer Science, Computer Engineering, Robotics, Electrical Engineering, Mathematics, Physics, or a related discipline.
Over 3 years of relevant industry experience in autonomous driving, robotics, perception, mapping, localization, sensor fusion, planning, active safety, or real-time embedded systems.
Strong system-level debugging ability, with experience analyzing complex autonomous driving issues from logs, replays, metrics, visualization tools, or vehicle testing.
Strong C/C++ programming skills, Solid background in 3D geometry, coordinate systems, state estimation, probabilistic modeling, numerical optimization, and real-time fusion systems.
Experience supporting production programs, vehicle integration, parameter tuning, validation, or release readiness across multiple platforms, carlines, or customers.
Ability to communicate technical analysis clearly in English. Strong collaboration and communication skills, including the ability to work effectively with global teams, OEM partners, and cross-functional stakeholders.
Ways To Stand Out From The Crowd
Background with map-perception fusion, HD/SD maps, online mapping, road topology, lane-level context, traffic signs/lights, or mapless/map-degraded driving.
Experience supporting multiple OEM programs, carlines, sensor configurations, map providers, or regional variants in production autonomous driving or ADAS projects.
Experience building automated triage, replay evaluation, quality metrics, regression testing, or cross-carline comparison workflows.
Background with BEV world models, occupancy grids, occlusion reasoning, static obstacle modeling, free-space estimation, or scenario understanding.
Experience with Kalman filters, particle filters, factor graphs, multi-object tracking, SLAM, localization, or map matching systems.
