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

About the Role

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.

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.
• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Compensation and Benefits

• Base salary range (Level 3): $152,000 USD - $241,500 USD
• Base salary range (Level 4): $184,000 USD - $287,500 USD
• Actual base salary will be determined based on your location, experience, and the pay of employees in similar positions.
• Eligible for equity and benefits.

Application Deadline

Applications for this job will be accepted at least until September 22, 2026. This posting is for an existing vacancy.

Equal Opportunity

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.