Senior Radar Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

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About the role

Intelligent machines powered by artificial intelligence—computers that can learn, reason, and interact with people—are transforming every industry. 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. We are seeking an exceptional Senior Radar Perception Engineer to help design and productize NVIDIA’s next-generation autonomous driving perception stack. You will work on the core 3D radar and multi-modal obstacle perception pipeline, contribute to architecture and algorithm design, and remain deeply hands-on with implementation, including modern transformer-based, radar-centric foundation models, and multi-sensor fusion techniques where they add real value. What you’ll be doing: • 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.  • Radar Perception Innovation: Conduct applied research on deep learning models to maximize the information content of radar point cloud data at every representation level. 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. Explore weakly-supervised pretraining and improve radar perception via large auto-labeled datasets. • 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. • Production Deep Learning: Build efficient, production-grade deep learning models: define objectives with the team, select and prototype architectures, run experiments, and follow best practices for training and evaluation, using techniques such as large-scale radar pretraining, cross-modal distillation (e.g., lidar-to-radar), and parameter-efficient fine-tuning (e.g., LoRA). • 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. • Data Strategy & Auto-Labeling: Contribute to the data strategy for radar perception: specify data and labeling requirements, help prioritize data collection and annotation, and collaborate with data and ground-truth teams, incorporating model-assisted workflows (e.g., active learning, automated radar labeling via lidar/camera foundation models) and model-in-the-loop tooling. • 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. What we need to see: • Industry Experience: 12+ years of hands-on experience developing deep learning–based perception, radar signal processing, or closely related systems for complex real-world problems, with strong proficiency in frameworks such as PyTorch and a track record of taking mo

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