This role pays more than
64%
of similar roles. Most pay
$194,075–$254,800
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$224k
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 Developer Relationship Manager - Autonomous Vehicles, you will serve as a primary technical advocate and ecosystem lead for developers building with autonomous vehicle technologies. You will manage engagement with OEMs, Tier 1 suppliers, startups, and research organizations to accelerate adoption across AI training, simulation, synthetic data generation, validation, and in-vehicle deployment. Your daily responsibilities include developing technical enablement programs like workshops and reference architectures, identifying technical blockers, and translating partner feedback into product roadmap priorities. You will utilize a technical toolkit including C++, Python, CUDA, and deep learning frameworks such as PyTorch or TensorFlow. The role focuses on solving complex challenges in the autonomous vehicle domain, specifically addressing the integration of AI models, sensor fusion, and safety workflows within an accelerated computing platform to build safer and more capable autonomous systems.
Lead technical engagement with autonomous vehicle partners including OEMs, Tier 1 suppliers, and research organizations.
Serve as the primary point of contact for developers adopting NVIDIA technologies across training, simulation, and deployment.
Create and deliver technical enablement programs such as workshops, reference architectures, sample applications, and demo content.
Identify technical blockers and gather feedback during early-access software integrations with key partners.
Advocate for partner requirements and product gaps to internal engineering teams to influence the product roadmap.
Translate complex technical capabilities into clear developer value and outcomes for go-to-market activities.
Track ecosystem adoption signals and engagement patterns to inform future developer programs and priorities.
What we're looking for
8+ years of experience in developer relations, technical partnerships, software engineering, product management, solutions architecture, or ecosystem development.
Strong technical background in autonomous vehicles, automotive software, AI, simulation, accelerated computing, embedded systems, or related domains.
Experience working with automotive OEMs, Tier 1 suppliers, AV startups, ISVs, or enterprise technology partners.
Hands-on experience with AI model training, synthetic data generation, simulation, data pipelines, full-stack software, edge deployment, or autonomous systems.
Ability to communicate technical concepts to developers and translate partner feedback into product requirements and roadmap inputs.
Proficiency in C++, Python, CUDA, and deep learning frameworks such as PyTorch or TensorFlow.
Understanding of AI model development lifecycles from data pipelines through optimization and deployment on edge devices.
Familiarity with automotive safety standards like ISO 26262 and experience with ADAS/AD software stacks.
Deep Learning
AI
Robotics
Autonomous Systems
CUDA
NeMo
Nemotron
Isaac Sim
Isaac GROOT
Alpamayo
NVIDIA Software Stack
SDKs
Architectural Design
Code Review
World Foundation Models
LLMs
VLMs
Multimodal Generative Models
Diffusion Models
Robotics
Autonomous Vehicles
Synthetic Data Generation
CUDA-X
Isaac Sim
Isaac Lab
OpenUSD
Omniverse
Deep Learning
Sim-to-Real
Video Generation
TensorRT
CUDA
C++
Python
PyTorch
ONNX
Triton
Deep Learning Inference
Model Optimization
Quantization
Pruning
Linux
ISO 26262
QNX
Real-time Systems
DriveOS
Hypervisors
PyTorch
JAX
TensorFlow
Python
C++
CUDA
Kubernetes
SLURM
Deep Learning
Reinforcement Learning
LLMs
MLOps
HPC
Distributed Training
Sim-to-Real
Natural Language Processing
Graphics
Python
C++
CUDA
PyTorch
TensorFlow
Machine Learning
Robotics
Autonomous Vehicles
Parallel Programming
VLMs
Closed-loop Training
AI Safety
Simulation
open-source
C++
Machine Learning
Robotics
autonomous vehicle s
Motion Planning
Trajectory Generation
Real-time Systems
Simulation
AI Infrastructure
Performance Optimization