NVIDIA has continuously reinvented itself over two decades. Our 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 — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.
The NVIDIA China Autonomous Driving Team is looking for a hands-on software engineer to shape how vehicles plan, decide, and act on the road. In this role, you will own the driving behavior of our autonomous driving stack across multiple production programs — performing deep root-cause analysis, adapting planning and control algorithms to diverse vehicle platforms, and building automation tools that scale our engineering impact globally. You will ride in test vehicles, tune real-world performance, and collaborate directly with OEM partners to deliver safe, comfortable, and production-ready autonomous driving behavior.
What you’ll be doing:
Own end-to-end Planning & Control integration across the NVIDIA AV software stack, including module interfaces, data flow, timing, and cross-team integration and validation with perception, localization, and vehicle actuation systems
Design, develop, and optimize planning and control algorithms for core L2, L2+, and L2++ driving scenarios, and drive their integration into the production software stack
Perform system integration, carline adaptation, and parameter tuning for planning and control modules across different vehicle platforms - adapting to diverse vehicle dynamics, steering systems, braking characteristics, sensor configurations, and OEM platform requirements
Build and maintain cross-carline integration validation workflows and comparison tooling to systematically evaluate integration behavior differences across vehicle platforms and accelerate multi-carline integration at scale
Improve the safety, comfort, feasibility, smoothness, and system-level robustness of the integrated planning and control system across different road geometries, traffic conditions, and vehicle platforms
Analyze, triage, and resolve cross-module Planning & Control integration and functional issues across multiple autonomous driving programs from L2 through L4
Perform integration root-cause analysis, bring-up validation, and performance tuning using simulation, replay tools, vehicle logs, on-vehicle diagnostics, and real-world testing, and provide clear resolution proposals
Collaborate with global teams and OEM partners to drive software integration, validation, release readiness, and production delivery; travel domestically and internationally for on-vehicle integration testing and customer collaboration as needed
What we need to see:
BS/MS in Computer Science, Electrical Engineering, Robotics, Vehicle Engineering, or a related field
5+ years of autonomous driving, ADAS, or robotics software development experience, with strong C++ and Python skills
Solid knowledge of motion planning, trajectory planning, vehicle control, vehicle dynamics/kinematics, or numerical optimization
Hands-on experience integrating planning and control modules into a production or prototype autonomous driving software stack
Experience with multi-carline adaptation, on-vehicle debugging, parameter tuning, simulation, log analysis, and production software development
Strong analytical, problem-solving, and English communication skills
Proficient English communication skills (written and verbal) — able to produce clear technical reports and participate in meetings with global teams
Ways to stand out from the crowd:
Production experience developing and integrating planning and control algorithms for autonomous driving or ADAS systems
Multi-carline development experience - adapting and tuning planning and control parameters across different vehicle dynamics, steering systems, and brake characteristics
Experience driving end-to-end Planning & Control integration across modules, vehicle platforms, or OEM programs, handling complex interactive scenarios such as lane changes, merging, yielding, cut-ins, and intersections, has knowledge of functional safety or autonomous driving validation standards
Expertise in path/speed planning, trajectory optimization, model predictive control, or constrained numerical optimization
Experience building developer tools, test automation frameworks, or cross-carline comparison workflows