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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.

• 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.

• 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 models from prototype to production.
• Data-Driven Workflows: Proven experience in data-driven development, including close collaboration with data, labeling, and ground-truth teams on radar data strategy, labeling quality, and iterative model improvement.
• Software Engineering: Strong programming skills in Python and/or C++, with experience building reliable, high-performance, production-quality software.
• Collaboration: Excellent communication and collaboration skills, with the ability to work effectively across multidisciplinary teams spanning AI, hardware, and safety engineering.
• 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.
• Embedded Optimization: Hands‑on experience architecting and deploying DNN-based perception pipelines on embedded or real-time platforms, including optimization for latency, memory, and compute constraints, and familiarity with modern architectures (e.g., Transformers, BEV networks).
• Signal Processing Depth: Deep understanding of radar physics and digital signal processing fundamentals (FMCW, beamforming, CFAR, micro-Doppler) and how to cleanly interface traditional signal processing outputs with downstream deep learning models.
• Academic/Research Track Record: Strong publication record or recognized contributions in deep learning, radar perception, multi-sensor fusion, or autonomous systems at leading conferences/journals (e.g., CVPR, ICCV, NeurIPS, IROS).
• 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.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000USD–356,500USD. You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July19,2026.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal‑opportunity employer. We do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.