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Senior Radar Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

NVIDIA· Engineer, Sys SW
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Hybrid (Santa Clara, CA), Hybrid (Remote (CA)), Remote$224k - $356.5kfull-timePosted 3mo ago

About the role

Senior role focusing on designing and productizing NVIDIA’s radar-based 3D obstacle perception stack for autonomous driving, including radar-centric foundation models and multi-sensor fusion.

Responsibilities

  • 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 radar point cloud information; address challenges like low angular resolution, multipath/ghost targets, micro-doppler, and class imbalance; explore weakly-supervised pretraining and large auto-labeled datasets.
  • Model Design & Fusion: Design and implement advanced 3D perception models using radar inputs and multi-sensor fusion (camera, radar, lidar) for obstacle detection, tracking, and BEV understanding.
  • Sensor & Stack Integration: Drive radar sensor evaluation and layout optimization to support L2-L4 autonomous driving with seamless multi-sensor fusion.
  • Production Deep Learning: Build production-grade models with objectives, architectures, experiments, and practices for training/evaluation; apply large-scale radar pretraining, cross-modal distillation, and parameter-efficient fine-tuning (e.g., LoRA).
  • KPIs & Error Analysis: Define and maintain KPI frameworks; analyze large datasets to identify radar-specific failure modes and improve accuracy, robustness, and efficiency.
  • Data Strategy & Auto-Labeling: Specify data/labeling requirements; prioritize data collection/annotation; collaborate with data/ground-truth teams; incorporate active learning and model-in-the-loop tooling.
  • Cross-Functional Productization: Collaborate with safety, systems, and software teams to ensure radar perception solutions meet safety, latency, and robustness requirements for deployment at scale.

Qualifications

  • Industry Experience: 12+ years of hands-on experience in deep learning–based perception, radar signal processing, or closely related systems; strong PyTorch proficiency and production track record.
  • Data-Driven Workflows: Experience with data-driven development and collaboration with labeling/ground-truth teams on radar data strategy and iterative model improvement.
  • Software Engineering: Proficiency in Python and/or C++, building reliable, high-performance production software.
  • Collaboration: Excellent communication and cross-disciplinary collaboration skills across AI, hardware, and safety engineering.
  • Education: BS/MS/PhD in Computer Science, Electrical Engineering, Robotics, or related fields (or equivalent experience).

Skills

Required:Deep learning for radar perceptionRadar signal processingMulti-sensor fusionPyTorchPython
Nice to have:C++

Benefits

  • equity
  • benefits
Last updated Oct 9, 2026. Interested? Apply on the company site.

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