About the job
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.
Responsibilities
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.
Conduct applied research on deep learning models to maximize the information content of radar point cloud data at every representation level.
Design and implement advanced 3D perception models utilizing radar inputs and multi-sensor fusion (camera, radar, lidar) for obstacle detection, tracking, and Bird’s-Eye-View (BEV) scene understanding.
Drive radar sensor evaluation, selection, and layout optimization to support L2-L4 autonomous driving applications, ensuring seamless multi-sensor fusion.
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.
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.
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.
Collaborate with safety, systems, and software teams to ensure radar perception solutions meet product requirements for safety, low latency, resource usage, and software robustness.
Qualifications
Minimum
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.
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.
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.
BS/MS/PhD in Computer Science, Electrical Engineering, Robotics, or related fields (or equivalent experience).
Preferred
Experience designing and deploying radar-based or multi-modal perception solutions for autonomous driving or robotics using deep learning at scale.
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).
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.
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).
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.