This role pays more than
99%
of similar roles. Most pay
$155,375–$219,612
— the shaded band above.
At the midpoint, this role pays about
$290k
versus about
$187k
for comparable roles.
Based on 240 similar postings.
Employer
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
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior Software Engineer - Autonomous Driving on the Automotive Platform Team, you will develop the software foundation for high-performance vehicle computing platforms supporting autonomous driving, ADAS, and digital cockpit architectures. You will lead architecture and technical strategy to optimize inference workloads, focusing on the deployment of deep neural networks on NVIDIA automotive compute platforms. Your daily work involves performing end-to-end performance analysis across DNN models, TensorRT flows, CUDA kernels, and memory behavior while implementing optimization techniques like quantization, pruning, and distillation. You will build tools for profiling and benchmarking while collaborating with silicon architecture and safety platform teams. Required skills include C/C++, Python, and deep familiarity with TensorRT, ONNX, PyTorch, CUDA, and Triton. The role addresses critical challenges in model compression, real-time systems, and safety-aware automotive deployment within the autonomous driving software stack.
What does a Software Engineer earn in California?
Median $214000 from 775 postings across 63 companies.
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.
Implement model optimization techniques including quantization, pruning, distillation, and graph optimization.
Optimize deep neural networks for latency, throughput, memory footprint, and power on NVIDIA automotive platforms.
Develop tools and methodologies for profiling, benchmarking, debugging, and validating platform performance.
Execute technical leadership across the autonomous driving software stack to ensure reliable and scalable deployment.
What we're looking for
BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
12+ years of software engineering experience in systems software, AI/ML infrastructure, deep learning inference, compiler/runtime technology, or platform performance.
Proficiency in C/C++ and practical Python programming.
Deep familiarity with TensorRT, TensorRT-LLM, ONNX, PyTorch, CUDA, Triton, or related frameworks.
Experience optimizing DNN models for latency, throughput, memory footprint, and power.
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.
Familiarity with ISO 26262, QNX, Safe RTOS, DriveOS, Linux, hypervisors, or virtualization.
C++
Machine Learning
Robotics
autonomous vehicle s
Motion Planning
Trajectory Generation
Real-time Systems
Simulation
AI Infrastructure
Performance Optimization
Reinforcement Learning
Python
C++
Deep Learning
GPU Clusters
Kubernetes
SLURM
High-Performance Computing
Data Pipelines
Simulation
Closed-Loop Simulation
PPO
GRPO
NuRec
Cosmos World Model
PyTorch
JAX
TensorFlow
Python
C++
CUDA
Kubernetes
SLURM
Deep Learning
Reinforcement Learning
LLMs
MLOps
HPC
Distributed Training
Sim-to-Real
Natural Language Processing
Graphics