NVIDIA
Autonomous Vehicles
SeniorSoftwareEngineer-AutonomousVehicles
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“Senior Software Engineer - Autonomous Vehicles at NVIDIA. Skills: Autonomous Vehicles, AI, ML, Robotics, Safety-Critical Systems, Real-time Systems, Planning, Trajectory Generation, C++. Design and integrate planning frameworks that combine end-to-end learned driving models with classical trajectory planning and deterministic safety systems. Develop runtime arbitration and safety enforcement mechanisms between AI-generated trajectories and rule-based safety constraints”
What You'll Achieve.
enable machines to perceive, reason, and act safely in the real world; productize learned driving models into deployable autonomous vehicle systems; ensure AI-generated trajectories are physically feasible, safe, explainable, and deployable on real automotive hardware platforms; ensure AI-generated behaviors satisfy vehicle dynamics, collision avoidance, passenger comfort, and safety requirements in real time; define how foundation-model and learning-based driving systems coexist with production-grade safety-critical vehicle platforms
Industry & Context.
solving some of the hardest problems in real-time robotics; Analyze and debug complex autonomy edge cases; solving deeply challenging engineering problems
What They're Looking For.
Must Have
software engineering fundamentals with production C++ development experience, understanding of autonomous vehicle planning, trajectory generation, motion planning, or robotics systems, Experience working with machine learning systems and understanding how learned models behave under uncertainty and real-world edge cases, Experience delivering scalable, production-quality systems from architecture through deployment, debugging, systems integration, and performance optimization skills for real-time systems, Excellent communication and cross-functional technical leadership abilities
Nice to Have
Experience deploying machine learning models into real-time embedded or robotics systems, Deep understanding of both classical planning systems and end-to-end learning approaches for autonomous driving, Experience with runtime safety validation, fallback systems, policy gating, or safety arbitration frameworks, Familiarity with foundation-model-based driving systems, learned planners, generative trajectory models, or AI-native autonomy stacks, intuition for bridging the gap between offline AI model capability and production deployment constraints, Experience with large-scale autonomy simulation, scenario replay, evaluation infrastructure, or safety validation pipelines, Passion for solving deeply challenging engineering problems at the intersection of AI, robotics, and real-world deployment
What You'll Do.
Design and integrate planning frameworks that combine end-to-end learned driving models with classical trajectory planning and deterministic safety systems
Develop runtime arbitration and safety enforcement mechanisms between AI-generated trajectories and rule-based safety constraints
Build scalable architecture enabling large AI driving models to operate reliably within automotive compute
and real-time execution constraints
Develop execution frameworks that ensure AI-generated behaviors satisfy vehicle dynamics
and safety requirements in real time
Define and implement safety-oriented planning capabilities including trajectory validation
runtime policy gating
and Minimum Risk Maneuver (MRM) strategies
Analyze and debug complex autonomy edge cases involving uncertainty
and real-world safety constraints
Improve observability
and debuggability across large-scale autonomy planning systems operating in simulation and on-vehicle environments
Drive architectural decisions balancing AI capability
and embedded deployment efficiency
Influence next-generation autonomy architecture defining how foundation-model and learning-based driving systems coexist with production-grade safety-critical vehicle platforms
How You'll Work.
Team & Collaboration
Partner closely with AI, planning, controls, and systems teams to productize learned driving models into deployable autonomous vehicle systems
Communication Scope
Excellent communication and cross-functional technical leadership abilities
Full Job Description
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation fueled by extraordinary technology—and exceptional people. Today, we’re harnessing the power of AI to build the next generation of autonomous machines. From self-driving cars to intelligent robots, NVIDIA is developing the full-stack platform that enables machines to perceive, reason, and act safely in the real world. We are seeking a Senior Software Engineer to help define the runtime intelligence and safety architecture behind next-generation autonomous driving systems. This role sits at the intersection of end-to-end AI driving models, vehicle dynamics, and safety-critical autonomy. Modern AI models can generate highly capable driving behaviors, but deploying them safely in production vehicles requires solving some of the hardest problems in real-time robotics: compute constraints, physical feasibility, uncertainty handling, runtime validation, and safety arbitration. You will build the framework that bridges large-scale learned driving models with deterministic planning and vehicle-level safety guardrails—ensuring AI-generated trajectories are physically feasible, safe, explainable, and deployable on real automotive hardware platforms. This role is ideal for engineers excited about bringing modern AI into real-world physical systems where latency, compute efficiency, vehicle dynamics, and safety constraints fundamentally matter. ## **What You’ll Be Doing:** * Design and integrate planning frameworks that combine end-to-end learned driving models with classical trajectory planning and deterministic safety systems. * Develop runtime arbitration and safety enforcement mechanisms between AI-generated trajectories and rule-based safety constraints. * Build scalable architecture enabling large AI driving models to operate reliably within automotive compute, latency, and real-time execution constraints. * Develop execution fram
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