About the job
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!
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.
Qualifications
Minimum
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.
Preferred
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