Manager, Deep Learning – Autonomous Vehicles and Robotics at NVIDIA

**Who this is for** An experienced technical leader with a background in deep learning, eager to drive innovation in autonomous vehicles and robotics by bridgin

Work type: onsite

Location: US, CA, Santa Clara

Type: Full-time

Summary

**Who this is for** An experienced technical leader with a background in deep learning, eager to drive innovation in autonomous vehicles and robotics by bridging the gap between cutting-edge research and production-scale deployment on NVIDIA edge hardware. **Key highlights** You will lead a high-impact engineering team to deliver model optimization solutions, working closely with automotive OEMs and internal hardware/compiler teams to shape the future of physical AI. **You might be a good fit if you...** - Possess 8+ years of experience with at least 5 years in DL optimization or neural network compilation. - Have 4+ years of successful team leadership experience in a high-growth environment. - Exhibit deep technical proficiency in modern model architectures like Transformers and inference toolchains like TensorRT. - Demonstrate a track record of managing customer engagements and delivering production-quality solutions.

Job Description

Join our Deep Learning Engineering team within NVIDIA's Tegra Solutions Engineering organization, where we deliver production-quality deep learning solutions for autonomous vehicles and robotics on edge hardware. As a key member of our team, you'll lead a group of highly skilled engineers. We work at the intersection of modern model architectures, compiler technology, and embedded deployment. Application areas include end-to-end autonomous driving, vision-language-action models, multi-camera perception, and robotic foundation models. You'll define and drive strategic technical initiatives, working directly with automotive OEMs and robotics partners to solve their toughest optimization challenges on NVIDIA DRIVE and Jetson platforms. You'll coordinate extensively with NVIDIA Research, hardware, and compiler teams to advance the state-of-the-art in deep learning for physical AI!

What you'll be doing:

Lead and develop a team of deep learning engineers delivering inference optimization and model enablement solutions for automotive and robotics customers.

Drive end-to-end technical engagements with OEM partners, owning scoping, resource allocation, and delivery of production-quality solutions.

Set technical direction on how modern architectures (transformers, vision-language models, state space models) are optimized and deployed on GPU and SOC platforms.

Partner with compiler, runtime, and hardware teams to connect customer workload patterns with platform capabilities and roadmap priorities.

Collaborate with NVIDIA Research and internal deep learning teams to bring brand new techniques into production!

Represent NVIDIA externally at partner reviews, conferences, and industry forums.

What we need to see:

Master's degree or equivalent experience in Computer Science, Electrical Engineering, or a related field.

8+ years of overall experience with at least 5 years in deep learning model optimization, inference engineering, or neural network compilation.

4+ years of team leadership experience

Proven ability to manage concurrent technical customer engagements and deliver under production constraints.

Strong knowledge of current DL architectures and inference optimization toolchains (TensorRT or equivalent).

Excellent communication skills with the ability to engage credibly with both OEM engineering leadership and deep technical ICs.

Ways to stand out from the crowd:

Experience leading DL optimization teams in the autonomous vehicle or robotics domain with direct OEM or Tier-1 engagement.

Background in training pipeline optimization, curriculum design, or end-to-end autonomous driving architectures.

Experience with ML compiler frameworks (TVM, MLIR, XLA, Triton) or inference runtime development.

Familiarity with automotive safety standards (ISO 26262, SOTIF) and their implications for inference system design.

Track record of building engineering teams in growing competitive talent markets and experience with Agentic AI frameworks, tools, and protocols like LangChain, LangGraph, MCP or equivalent experience

The Deep Learning Engineering team within Tegra Solutions Engineering sits at the intersection of NVIDIA's most advanced AI technology and the customers. We work end-to-end: from architecture decisions with OEM engineering leadership, through optimization and deployment on DRIVE and Jetson platforms, to production vehicles and robots operating in the field. Our engineers engage directly with the world's leading automotive and robotics companies, solving problems that span next-generation network architectures, training infrastructure, inference optimization, and closed-loop simulation. We collaborate closely with NVIDIA Research, various NVIDIA AI teams, and hardware teams. The team is growing, and we are investing in building out our presence across multiple sites. If you want your work to ship on real autonomous systems and shape the platforms they run on, this is the team to join.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 3, and 272,000 USD - 431,250 USD for Level 4.

You will also be eligible for equity and [benefits](https://www.nvidia.com/en-us/benefits/).

Applications for this job will be accepted at least until April 26, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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