Deep Learning Senior Engineer, End-to-end Autonomous Driving

NVIDIA NVIDIA · Semiconductors · Shanghai, China +1

NVIDIA is seeking a Deep Learning Senior Engineer to design, implement, and deploy end-to-end autonomous driving systems. The role focuses on AI 2.0, leveraging LLMs, VLMs, and VLAs for reasoning and planning in autonomous vehicles and robotics. Responsibilities include training large-scale models, building and fine-tuning LLM/VLM/VLA systems, exploring data generation strategies, and deploying models in production environments, integrating them with vehicle firmware.

What you'd actually do

  1. Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
  2. Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.
  3. Explore novel data generation and collection strategies to improve diversity and quality of training datasets.
  4. Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.
  5. Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.

Skills

Required

  • 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
  • proficiency with major deep learning frameworks
  • Familiarity with C++ for model deployment and integration in safety-critical systems.
  • PhD with 2+ years, MS (or equivalent experience) with 4+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.

Nice to have

  • 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 and strong track record of taking projects from concept to production deployment.

What the JD emphasized

  • deploy AI models in production environments
  • deploy production-grade ML models
  • production deployment

Other signals

  • LLMs
  • VLMs
  • VLAs
  • autonomous driving
  • robotics