Senior Deep Learning Engineer – End-To-End Autonomous Driving

NVIDIA

. Design and train innovative large-scale models, including generative, imitation, and reinforcement learning, to improve planning and reasoning capabilities .

Posted 10/6/2026full-timeRemote • California • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and training large-scale models, including LLMs, VLMs, and VLAs, for autonomous driving and robotics applications. Proven ability to deploy production-grade machine learning models while ensuring performance, safety, and reliability standards.

Highest-signal resume keywords

LLM/VLM/VLA DevelopmentDeep Learning ArchitecturesPython ProgrammingC++ IntegrationProduction Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Large-Scale Model TrainingGenerative LearningImitation LearningReinforcement LearningAlgorithm OptimizationDataset GenerationMachine Learning Model DeploymentBehavior PlanningMotion PlanningReal-Time Performance Optimization

Soft Skills

CollaborationProblem-Solving

Tools & Technologies

Deep Learning FrameworksVehicle Firmware

Certifications & Qualifications

PhD in Computer ScienceMS in Computer Engineering

Industry Keywords

Autonomous SystemsSelf-Driving TechnologyRoboticsSafety-Critical Software

Tech Stack

Tools & technologies

PythonC++

About the role

Key responsibilities & impact

Design and train innovative large-scale models, including generative, imitation, and reinforcement learning, to improve planning and reasoning capabilities
Build, pre-train, and fine-tune LLM/VLM/VLA systems for real-world autonomous driving and robotics applications
Explore novel data generation and collection strategies to improve training dataset diversity and quality
Collaborate with cross-functional teams to deploy AI models in production while meeting performance, safety, and reliability standards
Integrate machine learning models directly with vehicle firmware for production-quality, safety-critical software

Requirements

What you’ll need

Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems
Deep understanding of modern deep learning architectures and optimization techniques
Proven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale
Strong programming skills in Python and proficiency with major deep learning frameworks
Familiarity with C++ for model deployment and integration in safety-critical systems
PhD with 4+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field, or MS (or equivalent experience) with 6+ years of relevant experience
Experience with LLM/VLM/VLA systems deployable to autonomous vehicles or general robotics
Publications, open-source contributions, or competition wins related to LLM/VLM/VLA systems
Deep understanding of behavior and motion planning in real-world AV applications
Experience building and training large-scale datasets and models
Proven ability to optimize algorithms for real-time performance in resource-constrained environments
Strong track record of taking projects from concept to production deployment

Benefits

Comp & perks

Equity
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