Job description

NVIDIA is searching for a senior or principal engineer who specializes in building cutting-edge infrastructure for large-scale foundation model training in the Generalist Embodied Agent Research (GEAR) group. Our team is leading Project GR00T , NVIDIA’s moonshot initiative at building foundation models and full-stack technology for humanoid robots. You will work with an amazing and collaborative research team that consistently produces influential works on multimodal foundation models, large-scale robot learning, embodied AI, and physics simulation. Our past projects include Eureka , VIMA , Voyager , MineDojo , MimicPlay , Prismer , and more. Your contributions will have a significant impact on our research projects and product roadmaps. 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 job scheduling/orchestration tools (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. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and productive people in the world. Please join us and be part of the forefront of developing general-purpose robots and large-scale foundation models! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until January 13, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

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.
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 job scheduling/orchestration tools (e.g., SLURM, Kubernetes).
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.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until January 13, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aikubernetespythonllm

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