Nvidia Senior Research Engineer - Autonomous Vehicles 

United States, California 

841037161

What you will be doing:

• 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 videos, text, and sensor data;

• Build and optimize simulation infrastructure (based on GPU-accelerated simulators) to support the training of driving policies for AVs at scale;

• 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 cars;

• Propose scalable solutions that combine LLMs with policy learning.

• Apply reinforcement learning to finetune multimodal LLMs.

• Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows on large GPU clusters.

What we need to see:

• 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 like PPO, SAC, or Q-learning, including experience tuning hyperparameters and reward functions.

• Familiarity with common policy learning techniques like reward shaping, domain randomization, curriculum learning.

• Deep understanding of GPU acceleration, CUDA programming, and cluster management tools like 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 jobscheduling/orchestrationtools (e.g., SLURM, Kubernetes).