Senior Software Architect, Humanoid Robotics
at Nvidia
Posted 5 hours ago
No clicks
- Compensation
- Not specified
- City
- Shanghai
- Country
- China
Currency: Not specified
NVIDIA seeks a robotics-focused Solutions Architect to design, implement, and optimize scalable ML training pipelines for multimodal foundation models in robotics. You'll collaborate with researchers to integrate cutting-edge architectures into scalable training workflows, and develop data loaders for multimodal data (videos, text, sensor data). You will optimize GPU/cluster utilization for training and fine-tuning on large datasets and build monitoring/debugging tools to ensure reliable performance on large GPU clusters.
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.
