Senior Radar Perception Engineer, Obstacle Foundation Models – Autonomous Vehicles

🔥 8 hours ago

🏄 California – Remote

🏄 California – Remote

💵 $224k - $356.5k / year

⏰ Full Time

🟠 Senior

👷🏻‍♀️ Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 1%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 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