Manager, Deep Learning – Autonomous Vehicles and Robotics

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Manager for a Deep Learning Engineering team focused on delivering production-quality deep learning solutions for autonomous vehicles and robotics on edge hardware. The role involves leading a team, defining technical initiatives, and collaborating with automotive OEMs and robotics partners to optimize solutions on NVIDIA platforms, working at the intersection of model architectures, compiler technology, and embedded deployment.

What you'd actually do

  1. Lead and develop a team of deep learning engineers delivering inference optimization and model enablement solutions for automotive and robotics customers.
  2. Drive end-to-end technical engagements with OEM partners, owning scoping, resource allocation, and delivery of production-quality solutions.
  3. Set technical direction on how modern architectures (transformers, vision-language models, state space models) are optimized and deployed on GPU and SOC platforms.
  4. Partner with compiler, runtime, and hardware teams to connect customer workload patterns with platform capabilities and roadmap priorities.
  5. Collaborate with NVIDIA Research and internal deep learning teams to bring brand new techniques into production!

Skills

Required

  • 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 have

  • 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

What the JD emphasized

  • production-quality
  • production constraints
  • production vehicles
  • production-quality deep learning solutions
  • production vehicles and robots operating in the field

Other signals

  • production-quality deep learning solutions
  • autonomous vehicles and robotics
  • edge hardware
  • inference optimization
  • model enablement