Senior Robotics Foundation Model Training Architect

NVIDIA Corporation; Santa Clara (CA); On-site; USD 152,000 - 288,000; Full time

Job summary

NVIDIA Corporation is building a team to advance robotics foundation model training at scale, spanning data generation, multimodal model training, and deployment. We seek a hands-on Applied Engineer to work with researchers and production teams across interfaces, guiding experiments to production readiness.

You will architect and optimize end-to-end training workflows for robotics models, scale workloads across multi-GPU setups, and collaborate with product and engineering to shape Physical AI

Qualifications

• MS, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Electrical or Computer Engineering, Robotics, or a related field.
• 5+ years of industry or research experience in deep learning, distributed computing, or large-scale model training.
• Hands-on experience training or optimizing multimodal or foundation models, ideally in robotics settings.
• Experience across the AI model lifecycle, including pre-training, supervised fine-tuning, RL or other post-training methods, evaluation, and model optimization.
• Strong expertise in distributed training techniques (data/model/pipeline parallelism, sharding, check-pointing) on multi-GPU or multi-node systems.
• Expertise with multimodal training frameworks such as PyTorch, NVIDIA NeMo, JAX, or Hugging Face Transformers.
• Experience building or working with high-throughput data pipelines for large-scale training, including storage bandwidth, network throughput, and preprocessing.
• Strong communication skills with the ability to effectively collaborate across Researchers, Engineers and executives.

Responsibilities

• Architect and optimize end-to-end training workflows for robotics foundation models, like World Models, VLAs, WAMs.
• Build proof‑of‑concepts, reference architectures, and agentic workflows accelerating experimentation and model improvement.
• Scale pre-training, fine-tuning, and RL workloads across multi-GPU and multi-node systems.
• Identify and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets.
• Collaborate with NVIDIA product and engineering teams to provide feedback shaping future Physical AI platforms.

Skills

Deep learning

Distributed training

Multimodal models

PyTorch

NVIDIA NeMo

JAX

HuggingFace Transformers

Robotics

Data pipelines

Education

MS/PhD or equivalent

Tools

Nsight Systems

Nsight Compute

PyTorch Profiler

Job description

NVIDIA Corporation is building a team to advance robotics foundation model training at scale, spanning data generation, multimodal model training, and deployment. We seek a hands-on Applied Engineer to work with researchers and production teams across interfaces, guiding experiments to production readiness.

You will architect and optimize end-to-end training workflows for robotics models, scale workloads across multi-GPU setups, and collaborate with product and engineering to shape Physical AI