Manager, Deep Learning – Autonomous Vehicles and Robotics

Nvidia

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$224,000–$356,500 / yr
Posted
46 days ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $226k
This role $290k
$163k most similar roles pay here $377k

This role pays more than 91% of similar roles. Most pay $196,750–$254,587 — the shaded band above. At the midpoint, this role pays about $290k versus about $226k 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 985 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 971 roles with salary data.

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At a glance

TL;DR · Manager, Deep Learning – Autonomous Vehicles and Robotics

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As a Senior Deep Learning Engineering Manager at NVIDIA's Tegra Solutions Engineering, you will lead a team of engineers focused on delivering production-quality deep learning solutions for autonomous vehicles and robotics. Your responsibilities include driving technical engagements with automotive OEMs and robotics partners, setting strategic direction for model optimization and deployment on GPU and SOC platforms, and collaborating closely with internal teams to advance state-of-the-art techniques in deep learning. You will need a Master's degree or equivalent experience, at least 8 years of industry experience including 5 years in deep learning optimization, and strong knowledge of DL architectures and inference toolchains like TensorRT. Experience in the autonomous vehicle domain, ML compiler frameworks, automotive safety standards, and building engineering teams is highly desirable. This role offers a unique opportunity to work on cutting-edge AI technology at the intersection of modern model architectures and embedded deployment challenges.

Skills

TensorRT TVM MLIR XLA Triton Python CUDA C++ NVIDIA DRIVE Jetson ISO 26262 SOTIF LangChain LangGraph MCP CI/CD Git Jenkins Docker

What you'll do

  • Lead a team of deep learning engineers to deliver optimized solutions for automotive and robotics customers.
  • Define technical direction for deploying modern architectures on GPU and SOC platforms.
  • Coordinate with NVIDIA Research and hardware teams to advance deep learning capabilities.
  • Drive strategic initiatives and solve optimization challenges for OEM partners.
  • Represent NVIDIA externally at partner reviews, conferences, and industry forums.

What we're looking for

  • 8+ years of experience with deep learning model optimization and inference engineering.
  • At least 5 years of experience in neural network compilation or related field.
  • Proven ability to manage concurrent technical customer engagements under production constraints.
  • Strong knowledge of DL architectures and inference optimization toolchains like TensorRT.
  • Experience leading DL optimization teams in autonomous vehicle or robotics domain with OEM engagement.
  • Familiarity with automotive safety standards (ISO 26262, SOTIF) for inference system design.
  • Background in training pipeline optimization, curriculum design, or end-to-end autonomous driving architectures.

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