About the job
Intelligent machines powered by artificial intelligence are transforming transportation. NVIDIA is building the computing platforms, software, and AI systems that enable autonomous vehicles to perceive, reason, and act in complex environments. Our team develops NVIDIA’s end-to-end autonomous driving application. We are looking for a Senior Integration Engineer to accelerate the development, integration, evaluation, and deployment of end-to-end driving models across large-scale training infrastructure, simulation environments, and production vehicle platforms. In this role, you will work across model development, data, simulation, systems software, and vehicle engineering. You will help turn rapidly evolving AI models into reliable, high-performance autonomous driving functionality running on NVIDIA’s heterogeneous computing platforms.
Responsibilities
Integrate learned driving models with vehicle interfaces, sensor inputs, localization, mapping, safety systems, and other autonomous driving components.\\\nEstablish clear model input, output, timing, state-management, and runtime interface contracts.\\\nPartner with model developers to improve model quality, debuggability, runtime behavior, and readiness for deployment.\\\nInvestigate discrepancies between model behavior in development environments and on target vehicle platforms.\\\nOptimize model inference and surrounding software to meet latency, throughput, memory, determinism, and power requirements.\\\nDevelop tools and metrics for evaluating driving quality, safety, robustness, and regression performance at scale.\\\nPerform in-vehicle testing, collect and analyze driving data, and complete autonomous driving missions.\\\nDevelop high-quality production code in C++ and Python using CUDA and other GPU-accelerated technologies.
Qualifications
Minimum
PhD with 1+ year, MS with 3+ years, or BS (or equivalent experience) with 5+ years of relevant experience in Computer Science, Computer Engineering, Robotics, Machine Learning, or a related field.\\\nStrong C++ programming, software architecture, debugging, and performance-analysis skills, and model inference technologies such as CUDA and TensorRT.\\\nProficiency in Python and experience working with modern machine-learning frameworks such as PyTorch.\\\nExperience developing software on Linux and embedded or real-time operating systems such as QNX.\\\nExperience integrating machine-learning models into complex, performance-sensitive production systems.\\\nAbility to diagnose issues across model behavior, application software, middleware, operating systems, and hardware.\\\nExperience with autonomous driving, robotics, ADAS, or another real-time intelligent system.
Preferred
Experience deploying end-to-end driving, robotics, or embodied-AI models on production hardware.\\\nFamiliarity with model optimization, quantization, compilation, profiling, and hardware-aware neural-network design.\\\nA track record of turning research models into robust, measurable, and maintainable product functionality.\\\nSelf-motivation, sound engineering judgment, and a passion for solving cross-functional integration challenges.