Senior Perception Engineer – Obstacle Foundation Models, Autonomous Vehicles

NVIDIA

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

Posted 10/2/2026full-timeRemote • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying advanced 3D perception models for autonomous driving, utilizing deep learning frameworks and optimizing for performance metrics. Strong collaboration skills with cross-functional teams to ensure safety and efficiency in perception solutions.

Highest-signal resume keywords

Deep Learning Model Development3D Perception SystemsPyTorch Framework ProficiencyPython ProgrammingAutonomous Driving Solutions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep Learning3D Computer VisionMulti-Sensor FusionModel DeploymentData-Driven DevelopmentCUDA DevelopmentDNN-Based Perception PipelinesCamera ModelingMulti-View GeometryPerformance Optimization

Soft Skills

Excellent CommunicationCollaboration Skills

Tools & Technologies

PyTorchCUDAGPU Acceleration

Industry Keywords

Autonomous DrivingComputer VisionPerception SolutionsReal-Time PlatformsModel-Assisted Workflows

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Develop and improve the technical design, architecture, and roadmap for 3D obstacle perception supporting end-to-end autonomous driving
Design and implement advanced 3D perception models using multi-camera inputs and/or multi-sensor fusion for obstacle detection and tracking
Build efficient, production-grade deep learning models by defining objectives, selecting and prototyping architectures, running experiments, and applying training and evaluation best practices
Define and maintain KPI frameworks to quantify perception performance
Analyze large-scale real and synthetic datasets to identify failure modes and improve accuracy, robustness, and efficiency
Contribute to perception data strategy, including data and labeling requirements, collection and annotation priorities, and model-assisted workflows
Collaborate with data and ground-truth teams on active learning, auto-labeling, vision-language models, and model-in-the-loop tooling
Collaborate with safety, systems, and software teams to meet product requirements for safety, latency, resource usage, and software robustness
Prepare perception solutions for deployment at scale

Requirements

What you’ll need

PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field
Hands-on experience developing deep learning–based perception 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
Experience in data-driven development, including collaboration with data, labeling, and ground-truth teams
Strong programming skills in Python and/or C++
Experience building reliable, high-performance, production-quality software
Experience designing and deploying perception solutions for autonomous driving or robotics using camera-based deep learning at scale
Experience architecting and deploying DNN-based perception pipelines on embedded or real-time platforms
Experience optimizing for latency, memory, and compute constraints
Familiarity with CNNs, transformers, large-scale pretraining, parameter-efficient fine-tuning, LoRA, and vision-language models
Strong publication record or recognized contributions in deep learning, computer vision, or autonomous systems
Deep understanding of 3D computer vision fundamentals, camera modeling and calibration, multi-view geometry, and 3D representations
Experience with CUDA development and GPU-accelerated components
Excellent communication and collaboration skills

Benefits

Comp & perks

Equity
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