Senior Software Engineer, Autonomous Driving

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Employment: Full-time
Posted: 59 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Similar $183k

$119k $244k

below market most similar roles pay here above market

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior Software Engineer, Autonomous Driving

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The Senior Software Engineer - Autonomous Driving joins the Automotive Platform Team to build software foundations for high-performance vehicle computing platforms. In this technical leadership role, you will drive architecture and execution across the autonomous driving software stack, specifically focusing on optimizing and deploying deep neural networks on NVIDIA automotive compute platforms. Your daily responsibilities include leading performance analysis for DNN models, developing optimization techniques like quantization, pruning, and distillation, and building tools for profiling and benchmarking. You will utilize C++, Python, TensorRT, ONNX, PyTorch, CUDA, and Triton. The work involves solving complex system-level challenges at the intersection of deep learning inference, compiler/runtime technologies, and safety-aware automotive deployment to ensure fast, reliable, and efficient performance for autonomous driving and ADAS applications.

What does a Software Engineer earn in California?

Median $214500 from 1193 postings across 76 companies.

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Skills

TensorRT CUDA Python C++ Triton PyTorch ONNX TensorRT-LLM Deep Learning Inference Linux DriveOS QNX ISO 26262 Real-time Systems Hypervisors Virtualization

What you'll do

Lead the architecture and technical strategy for optimizing inference workloads in autonomous driving applications.
Perform end-to-end performance analysis across DNN models, TensorRT flows, CUDA kernels, and memory behavior.
Develop model optimization techniques including quantization, pruning, distillation, and graph optimization.
Optimize DNN models for latency, throughput, memory footprint, and power on automotive compute platforms.
Build tools and methodologies for profiling, benchmarking, debugging, and validating model and platform performance.
Execute deployment of deep neural networks on NVIDIA automotive compute platforms.

What we're looking for

BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a 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.
Hands-on experience with TensorRT internals, CUDA kernels, Triton kernels, or other compiler/runtime technologies (preferred).
Experience deploying optimized DNNs, LLMs, VLMs, or perception models on embedded, edge, robotics, or automotive platforms (preferred).
Background in autonomous driving, ADAS, robotics, real-time systems, safety-aware software, or deterministic low-latency systems (preferred).

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