Senior Research Engineer - Autonomous Vehicles

Details

We are recruiting top research engineers in the Autonomous Vehicles Research team at NVIDIA with strong expertise in software engineering and artificial intelligence (deep learning, reinforcement learning, and generative modeling). You must have strong programming skills, a solid track record of training deep learning models at scale, and a good mathematical foundation to analyze new AI algorithms. The team focuses on AI models for autonomous driving such as agent behavior models, end-to-end AV architectures, AI safety, closed-loop training approaches, and AV foundation models (VLMs, reasoning models, etc.). The role involves publishing at top venues and collaborating with the broader scientific community. Communicating with different teams and domain scientists is essential.

This position supports fundamental research with the freedom and bandwidth to conduct ground-breaking, publishable research while also impacting products and collaborating with teams focused on CUDA, physically-based simulation, graphics, natural language processing, autonomous driving, hardware optimization, robotics, healthcare, and more. NVIDIA fosters an open and collaborative research atmosphere.

Responsibilities

• Develop large-scale supervised learning and reinforcement learning training frameworks to support multi-modal foundation models for AVs capable of running on thousands of GPUs.
• Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
• Implement scalable data loaders and preprocessors tailored for multimodal datasets, including video, text, and sensor data.
• Build and optimize simulation infrastructure (based on GPU-accelerated simulators) to support large-scale training of driving policies.
• Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines.
• Develop sim-to-real transfer pipelines and work closely with the AV product team to deploy to real-world vehicles.
• Propose scalable solutions that combine LLMs with policy learning and apply reinforcement learning to fine-tune multimodal LLMs.
• Develop robust monitoring and debugging tools to ensure reliability and performance of training workflows on large GPU clusters.

Requirements

• Bachelor's degree in Computer Science, Robotics, Engineering, or a related field or equivalent experience.
• 10+ years of full-time industry experience in large-scale MLOps and AI infrastructure.
• Proven experience designing and optimizing distributed training systems with frameworks like PyTorch, JAX, or TensorFlow.
• Deep familiarity with reinforcement learning algorithms (PPO, SAC, Q-learning) including experience tuning hyperparameters and reward functions.
• Familiarity with policy learning techniques such as reward shaping, domain randomization, and curriculum learning.
• Deep understanding of GPU acceleration, CUDA programming, and cluster management tools (e.g., Kubernetes).
• Strong programming skills in Python and a high-performance language such as C++ for efficient system development.
• Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools (e.g., SLURM, Kubernetes).

Benefits

• Competitive base salary (range depends on level and location) and eligibility for equity and benefits.
• Opportunity to publish at top venues and collaborate across research and product teams.
• Work on large-scale, cutting-edge problems in autonomous driving, simulation, and AI infrastructure at NVIDIA.

Compensation

• Base salary range for Level 4: 184,000 USD - 287,500 USD.
• Base salary range for Level 5: 224,000 USD - 356,500 USD.
• You will also be eligible for equity and benefits.

Other details

• Location: Santa Clara, CA, United States (see locations field).
• Employment type: Full time.
• Applications accepted at least until October 11, 2025.
• NVIDIA is an equal opportunity employer and values diversity; no discrimination in hiring and promotion practices.