Senior Developer Relationship Manager, Autonomous Vehicles

Nvidia

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 59 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $225k

This role $236k

$172k $300k

below market most similar roles pay here above market

This role pays more than 68% of similar roles. Most pay $195,400–$254,750 — the blue band above. At the midpoint, this role pays about $236k versus about $225k 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 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 Developer Relationship Manager, Autonomous Vehicles

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The Senior Developer Relationship Manager - Autonomous Vehicles joins the team to lead developer ecosystem engagement for autonomous vehicle technologies. This role serves as a primary technical advocate for partners including OEMs, Tier 1 suppliers, and startups. You will develop technical enablement programs, workshops, reference architectures, and demos to accelerate adoption across AI training, simulation, synthetic data generation, validation, and in-vehicle deployment. You must translate partner feedback into product requirements and roadmap priorities while removing technical friction. Required skills include hands-on experience with C++, Python, CUDA, and deep learning frameworks like PyTorch or TensorFlow. The role addresses the technical challenges of building safer autonomous systems using accelerated computing platforms, including NVIDIA DRIVE, DGX, Omniverse, Cosmos, and CUDA-X for production-scale AI deployment.

Skills

CUDA Python C++ PyTorch TensorFlow NVIDIA DRIVE NVIDIA DGX NVIDIA Omniverse NVIDIA Cosmos NVIDIA Alpamayo NVIDIA AI Enterprise Deep Learning Synthetic Data Generation Edge Deployment Data Pipelines ISO 26262 ADAS Sensor Fusion Accelerated Computing

What you'll do

Lead technical developer engagement with strategic autonomous vehicle partners including OEMs, Tier 1 suppliers, and startups.
Help partners adopt NVIDIA technologies across AI training, simulation, synthetic data, validation, and in-vehicle deployment.
Develop and deliver technical enablement programs such as workshops, sample applications, reference architectures, and best-practice guides.
Support early-access software integrations by identifying technical blockers and collecting partner feedback.
Champion partner requirements, architectural patterns, and product gaps to NVIDIA’s internal product and engineering teams.
Translate technical capabilities into clear developer value and partner outcomes for go-to-market and product launch activities.
Track ecosystem needs and adoption signals to inform roadmap priorities and future developer programs.

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 engage credibly with developers and senior technical leaders while communicating clearly with business, product, and executive stakeholders.
Experience translating partner feedback into product requirements, technical roadmap input, documentation improvements, and scalable enablement programs.
Hands-on experience with C++, Python, CUDA, and deep learning frameworks such as PyTorch or TensorFlow.
Understanding of AI model development lifecycles, from data pipelines and training through optimization, validation, and deployment on edge devices.

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