NVIDIA

Deep Learning Engineer, End-To-End Autonomous Driving

NVIDIA

full-time

Posted on:

Origin:  • 🇺🇸 United States • California

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Salary

💰 $184,000 - $356,500 per year

Job Level

Mid-LevelSenior

Tech Stack

Python

About the role

  • Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
  • Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.
  • Explore novel data generation and collection strategies to improve diversity and quality of training datasets.
  • Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.
  • Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.
  • 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.
  • Master's degree or PhD (or equivalent experience).
  • 6+ years of work experience in AV or related field.
  • 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.

Requirements

  • 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.
  • Master's degree or PhD (or equivalent experience).
  • 6+ years of work experience in AV or related field.
  • 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.