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This role pays more than
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$168,250–$241,750
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At the midpoint, this role pays about
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versus about
$205k
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Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
JOB TITLE: Senior Radar Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles
The Senior Radar Perception Engineer joins the team to design and productize the next-generation autonomous driving perception stack. This role focuses on the core 3D radar and multi-modal obstacle perception pipeline, where the engineer will contribute to architecture and algorithm design while remaining hands-on with implementation. Key responsibilities include developing transformer-based, radar-centric foundation models, performing multi-sensor fusion for obstacle detection and Bird’s-Eye-View scene understanding, and conducting research to maximize radar point cloud information. The position requires proficiency in PyTorch, Python, and C++, along with experience in deep learning, radar signal processing, and GPU acceleration. The work addresses technical challenges like low angular resolution, multipath reflections, and ghost targets to ensure robust, low-latency perception for L2-L4 autonomous driving applications.
Develop the technical architecture and roadmap for radar-based 3D obstacle perception in autonomous driving.
Conduct research on deep learning models to maximize information content from radar point cloud data.
Design and implement 3D perception models for obstacle detection, tracking, and Bird’s-Eye-View scene understanding.
Build production-grade deep learning models using cross-modal distillation and parameter-efficient fine-tuning.
Drive radar sensor evaluation and layout optimization to support L2-L4 autonomous driving applications.
Maintain KPI frameworks to quantify performance and analyze large-scale datasets to identify and fix failure modes.
Define data and labeling requirements while incorporating model-assisted workflows like active learning.
Ensure perception solutions meet safety, latency, and resource usage requirements for large-scale deployment.
What we're looking for
BS/MS/PhD in Computer Science, Electrical Engineering, Robotics, or related fields (or equivalent experience).
12+ years of hands-on experience developing deep learning–based perception, radar signal processing, or closely related systems.
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 radar data strategy, labeling quality, and iterative model improvement.
Strong programming skills in Python and/or C++ to build reliable, high-performance, production-quality software.
Experience designing and deploying radar-based or multi-modal perception solutions for autonomous driving or robotics using deep learning at scale (preferred).
Hands-on experience architecting and deploying DNN-based perception pipelines on embedded or real-time platforms (preferred).
Deep understanding of radar physics and digital signal processing fundamentals (FMCW, beamforming, CFAR, micro-Doppler) (preferred).
Remote (Santa Clara, CA)+425 days ago
$184,000–$287,500
PyTorch
Python
C++
CUDA
Deep Learning
Computer Vision
Transformers
CNN
3D Perception
BEV
Multi-sensor Fusion
LoRA
Vision-Language Models
Active Learning
Model-in-the-loop
Data Strategy
Remote (Santa Clara, CA)25 days ago
$272,000–$431,250
Deep Learning
PyTorch
Python
C++
CUDA
Computer Vision
Transformers
CNN
3D Perception
BEV
Multi-sensor Fusion
LoRA
Active Learning
Model-in-the-loop
Representation Learning
Distillation
Large-scale Pretraining
Remote (Santa Clara, CA)+339 days ago
$184,000–$287,500
Python
C++
PyTorch
CUDA
Transformer
sensor-fusion
3D Perception
BEV
VLM
Distributed Training
Quantization
point-cloud
Voxel
Semantic Segmentation
Motion Prediction
Simulation