Nvidia Senior Research Engineer Foundation Model Training Infrastructure 

United States, California 

989367729

What you will be doing:

• Design and maintain large-scale distributed training systems to support multi-modal foundation models for robotics.

• 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.

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

• Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines.

What we need to see:

• Bachelor's degree in Computer Science, Robotics, Engineering, or a related field;

• 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 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).

Ways to stand out from the crowd:

• Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field;

• Demonstrated Tech Lead experience, coordinating a team of engineers and driving projects from conception to deployment;

• Strong experience at building large-scale LLM and multimodal LLM training infrastructure;

• Contributions to popular open-source AI frameworks or research publications in top-tier AI conferences, such as NeurIPS, ICRA, ICLR, CoRL.