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

NVIDIA Gruppe; Santa Clara (CA); On-site; USD 152,000 - 288,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA is building the computing platforms, software, and AI systems for autonomous vehicles. We seek a Senior Integration Engineer to accelerate development, integration, evaluation, and deployment of end-to-end driving models across training, simulation, and production vehicle platforms.

You will collaborate across model development, data, simulation, systems software, and vehicle engineering to deliver high-performance, production-ready driving functionality on NVIDIA GPUs.

Qualifications

• PhD with 1+ year, MS with 3+ years, or BS with 5+ years of relevant experience.
• Strong C++ programming, software architecture, debugging, and performance-analysis skills, and model inference technologies such as CUDA and TensorRT.
• Proficiency in Python and experience with PyTorch.

Responsibilities

• Integrate learned driving models with vehicle interfaces, sensor inputs, localization, mapping, safety systems, and other autonomous driving components.
• Establish clear model input, output, timing, state-management, and runtime interface contracts.
• Partner with model developers to improve model quality, debuggability, runtime behavior, and readiness for deployment.
• Investigate discrepancies 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 high-quality production code in C++ and Python using CUDA and other GPU-accelerated technologies.

Skills

C++ programming

Software architecture

Debugging

Performance analysis

Python

PyTorch

Linux

QNX

ML model integration

Autonomous driving

Education

PhD in CS/CE/Robotics

MS in CS/CE/Robotics

BS in CS/Engineering

Tools

CUDA

TensorRT

Linux

QNX

Job description

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.

What you’ll be doing:

• Integrate learned driving models with vehicle interfaces, sensor inputs, localization, mapping, safety systems, and other autonomous driving components.
• Establish clear model input, output, timing, state-management, and runtime interface contracts.
• Partner with model developers to improve model quality, debuggability, runtime behavior, and readiness for deployment.
• Investigate discrepancies 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 high-quality production code in C++ and Python using CUDA and other GPU-accelerated technologies.

What we need to see:

• 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, and model inference technologies such as CUDA and TensorRT.
• Proficiency in Python and experience working 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.

Ways to stand out from the crowd:

• 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.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 22, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.