This role pays more than
85%
of similar roles. Most pay
$199,012–$275,250
— the shaded band above.
At the midpoint, this role pays about
$290k
versus about
$237k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
Manager, Deep Learning – Autonomous Vehicles and Robotics joins the Deep Learning Engineering team within Tegra Solutions Engineering to lead a group of engineers delivering production-quality solutions for autonomous vehicles and robotics on edge hardware. This role involves leading technical engagements with automotive OEMs and robotics partners to solve optimization challenges on DRIVE and Jetson platforms. You will define strategic initiatives, manage resource allocation, and set the technical direction for optimizing modern architectures like transformers, vision-language models, and state space models for GPU and SOC deployment. The work focuses on inference optimization, model enablement, and bridging customer workload patterns with platform capabilities. Key requirements include expertise in deep learning model optimization, neural network compilation, and tools such as TensorRT or ML compiler frameworks like TVM, MLIR, XLA, and Triton to advance physical AI technologies.
Lead and develop a team of engineers to deliver inference optimization and model enablement solutions for automotive and robotics customers.
Manage end-to-end technical engagements with OEM partners, including scoping, resource allocation, and delivery of production-quality solutions.
Define the technical direction for optimizing and deploying modern architectures like transformers and vision-language models on GPU and SoC platforms.
Coordinate with compiler, runtime, and hardware teams to align customer workload patterns with platform capabilities and roadmap priorities.
Collaborate with NVIDIA Research to integrate new deep learning techniques into production environments.
Represent NVIDIA at partner reviews, conferences, and industry forums to communicate technical solutions and product capabilities.
What we're looking for
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 like TensorRT.
Excellent communication skills for engaging with both executive leadership and deep technical individuals.
Experience in the autonomous vehicle or robotics domain with direct OEM or Tier-1 engagement is preferred.
Familiarity with ML compiler frameworks (TVM, MLIR, XLA, Triton) or automotive safety standards is preferred.
Reinforcement Learning
Python
C++
Deep Learning
GPU Clusters
Kubernetes
SLURM
High-Performance Computing
Data Pipelines
Simulation
Closed-Loop Simulation
PPO
GRPO
NuRec
Cosmos World Model
TensorRT
CUDA
C++
Python
PyTorch
ONNX
Triton
Deep Learning Inference
Model Optimization
Quantization
Pruning
Linux
ISO 26262
QNX
Real-time Systems
DriveOS
Hypervisors