Senior Software Architect, Humanoid Robotics

NVIDIA accelerates humanoid robots’ development with the Isaac solution and GR00T blueprint. We’re now looking for a robotics expert, especially in Foundation Model to support this effort. As a Solutions Architect, you’ll collaborate with an exceptional and highly collaborative research team known for influential work in multimodal foundation models, large-scale robot learning, embodied AI, and physics simulation, pushing the frontier of humanoid robotics.

What You Will Be Doing

• Design, implement, and optimize scalable ML training pipelines for training multimodal foundation models for robotics.
• Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines.
• Implement scalable data loaders and preprocessors for multimodal datasets, such as videos, text, and sensor data.
• Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
• 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.
• 3+ 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 VLA, VLM, large-scale LLM and multimodal LLM training infrastructure.
  
  

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