Senior Software Engineer - Autonomous Driving

NVIDIA Gruppe; Santa Clara (CA); On-site; USD 224,000 - 356,500; Full time

Job summary

NVIDIA Gruppe is looking for an exceptional engineer for their Automotive Platform Team to lead the architecture and optimize software for autonomous driving technologies. Candidates should have 12+ years experience in software engineering with a strong focus on deep learning performance and familiarity with programming in C/C++ and Python.

This role involves collaboration across multiple technical teams and will leverage advanced model optimization techniques. The position offers a competitive salary range of 224,000 USD - 356,500 USD along with equity and benefits.

Qualifications

• 12+ years of software engineering experience in systems software, AI/ML infrastructure.
• Strong experience optimizing DNN models for performance.
• Hands-on experience with CUDA kernels or related technologies.

Responsibilities

• Lead architecture and technical strategy for autonomous driving applications.
• Drive end-to-end performance analysis across various models and technologies.
• Develop optimization techniques for deep learning models.

Skills

C/C++

Python

TensorRT

CUDA

Deep Learning

Education

BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering

Tools

TensorRT

PyTorch

ONNX

Triton

Job description

Our Automotive Platform Team is building the software foundation for scalable, high-performance vehicle computing platforms that power autonomous driving, ADAS, digital cockpit, and centralized vehicle architectures. We are looking for exceptional engineers who thrive on solving deeply complex system-level challenges and shaping the future of automotive computing.

What you'll be doing:

• Lead architecture and technical strategy for optimizing inference workloads in autonomous driving applications.
• Drive end-to-end performance analysis across DNN models, TensorRT/compiler flows, CUDA kernels, memory behavior, scheduling, runtime services, and automotive platform constraints.
• Develop and guide model optimization techniques such as quantization, pruning, distillation, graph optimization, operator fusion, kernel selection, and layout/memory optimization.
• Collaborate with TensorRT, CUDA, compiler, silicon architecture, perception, planning, DriveOS and safety platform teams.
• Build tools, methodologies, and metrics for profiling, benchmarking, debugging, and validating model and platform performance.

What we need to see:

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
• 12+ years of software engineering experience in systems software, AI/ML infrastructure, deep learning inference, compiler/runtime technology, or platform performance.
• Strong C/C++ and practical Python experience.
• Deep familiarity with TensorRT, TensorRT-LLM, ONNX, PyTorch, CUDA, Triton, or related frameworks.
• Experience optimizing DNN models for latency, throughput, memory footprint, and power.

Ways to stand out from the crowd:

• Hands-on experience with TensorRT internals, CUDA kernels, Triton kernels, or other compiler/runtime technologies.
• Experience deploying optimized DNNs, LLMs, VLMs, or perception models on embedded, edge, robotics, or automotive platforms.
• Background in autonomous driving, ADAS, robotics, real-time systems, safety-aware software, or deterministic low-latency systems.
• Experience with ISO 26262, QNX, Safe RTOS, DriveOS, Linux, hypervisors, or virtualization.

Salary & benefits:

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD. You will also be eligible for equity and benefits.

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.