Senior Software Engineer, Context Fusion and Multi-Vehicle Adaptation - Autonomous Vehicles

NVIDIA has continuously reinvented itself over two decades. NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI, with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive, reason, and understand the world. This is our life’s work: to amplify human imagination and intelligence. The NDAS CN TSE team is looking for a hands-on software engineer to support Context Fusion delivery across multiple OEM programs and vehicle platforms, based in Beijing or Shanghai. In this role, you will analyze real-world fusion issues, adapt and tune Context Fusion behavior across different carlines, build scalable debugging and validation workflows, and collaborate with global engineering teams and OEM partners to deliver safe, reliable, and production-ready autonomous driving features. You will work at the intersection of perception, map, localization, ego motion, vehicle signals, active safety, and system integration. This role requires strong system-level debugging ability, practical autonomous driving engineering experience, and the willingness to work directly with test vehicles, customer programs, and cross-geo stakeholders. What You’ll Be Doing: Analyze, triage, and resolve complex Context Fusion issues across multiple NDAS programs and carlines, using replay tools, log-based diagnostics, and on-vehicle debugging.Develop and improve fusion logic that integrates perception outputs, map signals, and vehicle state into reliable driving-relevant environment representations. 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.