Manager, Deep Learning – Autonomous Vehicles and Robotics

NVIDIA•Santa Clara, CA

About The Position

Join NVIDIA's Deep Learning Engineering team within the Tegra Solutions Engineering organization, which focuses on delivering production-quality deep learning solutions for autonomous vehicles and robotics on edge hardware. This role involves leading a team of highly skilled engineers 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. The manager will define and drive strategic technical initiatives, working directly with automotive OEMs and robotics partners to solve optimization challenges on NVIDIA DRIVE and Jetson platforms. Extensive coordination with NVIDIA Research, hardware, and compiler teams is required to advance the state-of-the-art in deep learning for physical AI. The Deep Learning Engineering team works 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. Engineers engage directly with leading automotive and robotics companies, addressing problems across next-generation network architectures, training infrastructure, inference optimization, and closed-loop simulation. The team collaborates closely with NVIDIA Research, various NVIDIA AI teams, and hardware teams, and is expanding its presence across multiple sites. This is an opportunity to contribute to real autonomous systems and shape the platforms they run on.

Requirements

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.

Nice To Haves

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

Responsibilities

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.