Senior Radar Perception Engineer, Obstacle Foundation Models – Autonomous Vehicles

Job Description:

• Develop and improve the technical design, architecture, and roadmap for radar-based 3D obstacle perception supporting end-to-end autonomous driving
• Conduct applied research on deep learning models to maximize information from radar point cloud data
• Address radar perception challenges including low and non-uniform angular resolution, multipath and ghost targets, micro-Doppler signatures, and class imbalance
• Explore weakly-supervised pretraining and improve radar perception using large auto-labeled datasets
• Design and implement 3D perception models using radar inputs and camera, radar, and lidar fusion
• Develop obstacle detection, tracking, and Bird’s-Eye-View scene understanding systems
• Drive radar sensor evaluation, selection, and layout optimization for L2-L4 autonomous driving applications
• Build efficient, production-grade deep learning models from objective definition and architecture prototyping through experimentation, training, and evaluation
• Apply large-scale radar pretraining, cross-modal distillation, and parameter-efficient fine-tuning
• Define and maintain KPI frameworks for radar perception performance
• Analyze real and synthetic datasets to identify radar-specific failure modes and improve accuracy, robustness, and efficiency
• Contribute to radar data strategy, data collection and annotation prioritization, and model-assisted auto-labeling workflows
• Collaborate with data and ground-truth teams
• Collaborate with safety, systems, and software teams to ensure product requirements for safety, latency, resource usage, robustness, and scalable deployment are met

Requirements:

• 12+ years of hands-on experience developing deep learning–based perception, radar signal processing, or closely related systems for complex real-world problems
• Strong proficiency in frameworks such as PyTorch
• Track record of taking models from prototype to production
• Proven experience in data-driven development, including 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++
• Experience building reliable, high-performance, production-quality software
• BS/MS/PhD in Computer Science, Electrical Engineering, Robotics, or related fields, or equivalent experience
• 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
• Optimization for latency, memory, and compute constraints
• Familiarity with modern architectures such as Transformers and BEV networks
• Deep understanding of radar physics and digital signal processing fundamentals, including FMCW, beamforming, CFAR, and micro-Doppler
• Strong publication record or recognized contributions in deep learning, radar perception, multi-sensor fusion, or autonomous systems at leading conferences/journals
• Experience with CUDA development and optimizing training or inference pipelines through custom CUDA kernels or other GPU-accelerated components
• Excellent communication and collaboration skills across multidisciplinary AI, hardware, and safety engineering teams

Benefits:

• Equity
• Benefits