Manager, Deep Learning – Autonomous Vehicles and Robotics
About The Position
The Manager, Deep Learning leads a team in NVIDIA's Tegra Solutions Engineering organization, delivering production-quality deep learning solutions for autonomous vehicles and robotics on edge hardware. The role sits at the intersection of modern model architectures, compiler technology, and embedded deployment, with application areas including end-to-end autonomous driving, vision-language-action models, multi-camera perception, and robotic foundation models. The manager defines and drives strategic technical initiatives, leads technical engagements with automotive OEMs and robotics partners, and coordinates with NVIDIA Research, hardware, compiler, runtime, and internal deep learning teams. The team works end-to-end, from architecture decisions and training infrastructure through model optimization and deployment on DRIVE and Jetson platforms, to production systems in vehicles and robots. The role also represents NVIDIA externally at partner reviews, conferences, and industry forums.
Requirements
- Master's degree or equivalent experience in Computer Science, Electrical Engineering, or a related field.
- 8+ years of overall experience, including 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 deep learning architectures and inference optimization toolchains such as TensorRT or equivalent.
- Excellent communication skills and ability to engage credibly with OEM engineering leadership and deep technical individual contributors.
Nice To Haves
- Experience leading deep learning 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 such as TVM, MLIR, XLA, or Triton, or with inference runtime development.
- Familiarity with automotive safety standards such as ISO 26262 and SOTIF and their implications for inference system design.
- Track record of building engineering teams in growing competitive talent markets.
- Experience with Agentic AI frameworks, tools, and protocols such as LangChain, LangGraph, MCP, or equivalent.
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, including scoping, resource allocation, and delivery of production-quality solutions.
- Set technical direction for optimizing and deploying modern architectures, including transformers, vision-language models, and state space models, 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 new techniques into production.
- Represent NVIDIA externally at partner reviews, conferences, and industry forums.
Benefits
- Equity
- Benefits
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What This Job Offers
Job Type
Full-time
Career Level
Manager
