Senior Radar Perception & Fusion Engineer

NVIDIA; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

NVIDIA is seeking a Senior Radar Perception Engineer to architect and productize the radar-based 3D obstacle perception stack for autonomous driving. You will work on end-to-end perception with radar, camera, and lidar inputs, driving model development, data strategies, and deployment readiness.

You will lead research and production-grade implementation, focusing on scalable transformer-based models and efficient fusion techniques to improve accuracy, latency, and robustness in real-world

Qualifications

• 12+ years of hands-on experience developing deep learning-based perception, radar signal processing, or closely related systems.
• Strong proficiency in PyTorch and a track record of moving models from prototype to production.
• BS/MS/PhD in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent experience).

Responsibilities

• Architect and roadmap radar-based 3D obstacle perception for end-to-end autonomous driving functionalities using state-of-the-art DNN and transformer-based architectures.
• Conduct applied research to maximize information content of radar data and tackle issues like low angular resolution and multipath reflections.
• Design and implement 3D perception models using radar inputs and multi-sensor fusion (camera, radar, lidar) for detection, tracking, and BEV understanding.

Skills

Deep learning perception

Radar signal processing

Python/C++ software engineering

Education

BS/MS/PhD in CS/EE/Robotics

Tools

PyTorch

Python

C++

Job description

NVIDIA is seeking a Senior Radar Perception Engineer to architect and productize the radar-based 3D obstacle perception stack for autonomous driving. You will work on end-to-end perception with radar, camera, and lidar inputs, driving model development, data strategies, and deployment readiness.

You will lead research and production-grade implementation, focusing on scalable transformer-based models and efficient fusion techniques to improve accuracy, latency, and robustness in real-world