> Markdown version of [/jobs/ext/2307490-senior-software-engineer-autonomous-vehicles](https://www.wearedevelopers.com/jobs/ext/2307490-senior-software-engineer-autonomous-vehicles). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer - Autonomous Vehicles - **Company:** NVIDIA Corporation - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $224,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Software Debugging, Machine Learning, Motion Planning, Performance Tuning, Software Systems, Systems Integration, AI Infrastructure, Real Time Systems, Information Technology, Machine Learning Operations - **Published:** August 30, 2026 - **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Software-Engineer---Autonomous-Vehicles_JR2014431 ## About the Role * BS, MS, or PhD (or equivalent experience) in Computer Science, Robotics, Electrical Engineering, AI/ML, or related technical field. * 12+ years of relevant industry experience in autonomous systems, robotics, AI infrastructure, or safety-critical software systems. * Strong software engineering fundamentals with production C++ development experience. * Strong 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. * Strong debugging, systems integration, and performance optimization skills for real-time systems. * Excellent communication and cross-functional technical leadership abilities. Ways To Stand Out From The Crowd: * 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. * Strong 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. ## Description 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 frameworks that ensure AI-generated behaviors satisfy vehicle dynamics, collision avoidance, passenger comfort, and safety requirements in real time. * Define and implement safety-oriented planning capabilities including trajectory validation, fallback handling, runtime policy gating, and Minimum Risk Maneuver (MRM) strategies. * Partner closely with AI, planning, controls, and systems teams to productize learned driving models into deployable autonomous vehicle systems. * Analyze and debug complex autonomy edge cases involving uncertainty, model failure modes, planner disagreement, and real-world safety constraints. * Improve observability, reliability, and debuggability across large-scale autonomy planning systems operating in simulation and on-vehicle environments. * Drive architectural decisions balancing AI capability, system robustness, safety, 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. ## Related Videos - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [Rethinking Intelligence: AI, Accessibility, and the Future of Inclusive Work - Artur Ortega](https://www.wearedevelopers.com/videos/1377-rethinking-intelligence-ai-accessibility-and-the-future-of-inclusive-work-artur-ortega) - [Shift Left On Accessibility - Geri Reid](https://www.wearedevelopers.com/videos/1712-shift-left-on-accessibility-geri-reid) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) ## Related Articles - [How software is steering vehicle technology](https://www.wearedevelopers.com/magazine/515-how-software-is-steering-vehicle-technology) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)