Senior Integration Engineer, End-to-End Model - Autonomous Vehicles

NVIDIA•Santa Clara, CA

3d•$152,000 - $287,500

About The Position

NVIDIA is seeking a Senior Integration Engineer to support development, integration, evaluation, and deployment of end-to-end autonomous driving models across large-scale training infrastructure, simulation environments, and production vehicle platforms. The role works across model development, data, simulation, systems software, and vehicle engineering to turn evolving AI models into reliable, high-performance autonomous driving functionality on NVIDIA heterogeneous computing platforms.

Requirements

PhD with at least 1 year, MS with at least 3 years, or BS (or equivalent experience) with at least 5 years of relevant experience in Computer Science, Computer Engineering, Robotics, Machine Learning, or a related field.
Strong C++ programming, software architecture, debugging, and performance-analysis skills, and experience with model inference technologies such as CUDA and TensorRT.
Proficiency in Python and experience with modern machine-learning frameworks such as PyTorch.
Experience developing software on Linux and embedded or real-time operating systems such as QNX.
Experience integrating machine-learning models into complex, performance-sensitive production systems.
Ability to diagnose issues across model behavior, application software, middleware, operating systems, and hardware.
Experience with autonomous driving, robotics, ADAS, or another real-time intelligent system.

Nice To Haves

Experience deploying end-to-end driving, robotics, or embodied-AI models on production hardware.
Familiarity with model optimization, quantization, compilation, profiling, and hardware-aware neural-network design.
A track record of turning research models into robust, measurable, and maintainable product functionality.
Self-motivation, sound engineering judgment, and a passion for solving cross-functional integration challenges.

Responsibilities

Integrate learned driving models with vehicle interfaces, sensor inputs, localization, mapping, safety systems, and other autonomous driving components.
Establish model input, output, timing, state-management, and runtime interface contracts.
Partner with model developers to improve model quality, debuggability, runtime behavior, and deployment readiness.
Investigate differences between model behavior in development environments and on target vehicle platforms.
Optimize model inference and surrounding software to meet latency, throughput, memory, determinism, and power requirements.
Develop tools and metrics for evaluating driving quality, safety, robustness, and regression performance at scale.
Perform in-vehicle testing, collect and analyze driving data, and complete autonomous driving missions.
Develop production code in C++ and Python using CUDA and other GPU-accelerated technologies.