Nvidia ITE

Senior Radar Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

US, CA, Santa Clara · Senior

Sponsorship not specifiedDetected 6 days ago

PythonC++AngularAlgorithmsDeep LearningPyTorchNLPRoboticsElectrical EngineeringSignal ProcessingSensorsResearchCommunicationCollaboration

About the role

GPU-accelerated deep learning provides the foundation for machines to perceive, reason, and solve complex problems.
NVIDIA GPUs run deep learning algorithms that simulate aspects of human intelligence, acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.
Tackle radar perception's hardest problems: low and non-uniform angular resolution, multipath and ghost targets, micro-doppler signatures for small targets, and severe class imbalance.

Architecture & Roadmap: Develop and improve the technical design, architecture, and roadmap for radar-based 3D obstacle perception to support end-to-end autonomous driving functionalities, leveraging state-of-the-art DNN and transformer-based architectures.
Model Design & Fusion: Design and implement advanced 3D perception models utilizing radar inputs (ranging from low-level range-doppler/azimuth-elevation maps to sparse/dense point clouds) and multi-sensor fusion (camera, radar, lidar) for obstacle detection, tracking, and Bird's-Eye-View (BEV) scene understanding.
Sensor & Stack Integration: Drive radar sensor evaluation, selection, and layout optimization to support L2-L4 autonomous driving applications, ensuring seamless multi-sensor fusion.
KPIs & Error Analysis: Help define and maintain KPI frameworks to quantify radar perception performance; analyze large-scale real and synthetic datasets to identify failure modes unique to radar (e.g., multipath reflections, clutter, ghost objects) and systematically improve accuracy, robustness, and efficiency.
Cross-Functional Productization: Collaborate with safety, systems, and software teams to ensure radar perception solutions meet product requirements for safety, low latency, resource usage, and software robustness, and are ready for deployment at scale.
Software Engineering: Strong programming skills in Python and/or C++, with experience building reliable, high-performance, production-quality software.

Excellent communication and collaboration skills, with the ability to work effectively across multidisciplinary teams spanning AI, hardware, and safety engineering.
GPU Acceleration: Experience with CUDA development and optimizing training or inference pipelines through custom CUDA kernels or other GPU-accelerated components to handle high-bandwidth raw radar or tensor data.
Experience with CUDA development and optimizing training or inference pipelines through custom CUDA kernels or other GPU-accelerated components to handle high-bandwidth raw radar or tensor data.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
The base salary range is 224,000 USD - 356,500 USD.

You will also be eligible for equity and benefits.
Radar Perception Innovation: Conduct applied research on deep learning models to maximize the information content of radar point cloud data at every representation level.
Education: BS/MS/PhD in Computer Science, Electrical Engineering, Robotics, or related fields (or equivalent experience).
Radar & Multi-Modal Scale: Experience designing and deploying radar-based or multi-modal perception solutions for autonomous driving or robotics using deep learning at scale.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.
equal opportunity employer.

This listing is sourced directly from Nvidia ITE's careers page and normalized into a canonical job model.