Senior Radar Perception Engineer, Obstacle Foundation Models – Autonomous Vehicles

NVIDIA

. Develop and improve the technical design, architecture, and roadmap for radar-based 3D obstacle perception supporting end-to-end autonomous driving .

Posted 10/2/2026full-timeRemote • California • United StatesSenior💰 $224,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing deep learning models for radar-based 3D obstacle perception in autonomous driving, with a strong focus on data-driven development and collaboration across multidisciplinary teams.

Highest-signal resume keywords

Deep Learning Model DevelopmentRadar Signal ProcessingPython ProgrammingPyTorch FrameworkDNN-Based Perception Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningRadar PerceptionData-Driven DevelopmentModel OptimizationDigital Signal ProcessingCUDA DevelopmentMulti-Modal Perception SolutionsEmbedded SystemsFMCWBeamforming

Soft Skills

Excellent CommunicationCollaboration Skills

Tools & Technologies

PyTorchCUDATransformersBEV Networks

Certifications & Qualifications

BS/MS/PhD in Computer ScienceElectrical EngineeringRobotics

Industry Keywords

Autonomous Driving3D Obstacle PerceptionRadar Data StrategyMulti-Sensor FusionReal-Time Platforms

Tech Stack

Tools & technologies

AngularCloudPythonPyTorchC++

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

Equity
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