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

NVIDIA; Santa Clara (CA); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA in Santa Clara, CA is seeking exceptional engineers to join our autonomous driving team to design, implement, and deploy end-to-end autonomous driving systems running on NVIDIA chips in mass-production vehicles.

You will build and train large-scale models, deploy in production, and collaborate across teams to ensure safety, performance, and reliability in real-world environments.

Qualifications

• Hands-on experience building LLMs, VLMs, or VLAs from scratch.
• Deep understanding of modern deep learning architectures.
• Proven record of deploying production 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 in safety-critical systems.
• PhD with 4+ years, MS with 6+ years of relevant experience in CS/CE or related field.

Responsibilities

• Design and train innovative large-scale models to improve planning and reasoning of driving systems.
• Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics.
• Explore novel data generation and collection strategies to improve training data quality and diversity.
• Collaborate with cross-functional teams to deploy AI models in production environments with safety and reliability.
• Integrate ML models directly with vehicle firmware to deliver production-quality software.

Skills

LLMs

VLMs

VLAs

Python

C++

Deep learning

Deployment

Education

PhD in CS/EE

MS in CS/CE

Tools

PyTorch

TensorFlow

CUDA

Job description

NVIDIA in Santa Clara, CA is seeking exceptional engineers to join our autonomous driving team to design, implement, and deploy end-to-end autonomous driving systems running on NVIDIA chips in mass-production vehicles.

You will build and train large-scale models, deploy in production, and collaborate across teams to ensure safety, performance, and reliability in real-world environments.