Senior Integration Engineer, End-to-End Model - Autonomous Vehicles
$152k - $288k • Remote • US, CA, Santa Clara
Posted 1mo ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
NVIDIA is seeking a Senior Integration Engineer to join their autonomous driving team. This role focuses on accelerating the development, integration, evaluation, and deployment of end-to-end driving models. You will collaborate across model development, data, simulation, systems software, and vehicle engineering teams to transform evolving AI models into reliable autonomous driving functionality on NVIDIA's computing platforms. The position involves working with large-scale training infrastructure, simulation environments, and production vehicle platforms.
Responsibilities
- Integrate learned driving models with vehicle interfaces, sensor inputs, localization, mapping, safety systems, and other autonomous driving components.
- Establish clear contracts for model input, output, timing, state-management, and runtime interfaces.
- Partner with model developers to enhance model quality, debuggability, runtime behavior, and deployment readiness.
- Investigate discrepancies between model behavior in development and on target vehicle platforms.
- Optimize model inference and surrounding software for 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 high-quality production code in C++ and Python using CUDA and other GPU-accelerated technologies.
Requirements
- 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.
- Strong C++ programming, software architecture, debugging, and performance-analysis skills.
- Experience with model inference technologies such as CUDA and TensorRT.
- Proficiency in Python and experience with modern machine-learning frameworks like PyTorch.
- Experience developing software on Linux and embedded or real-time operating systems like 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.
Benefits
- Equity
- Benefits