Senior Robotics AI Foundation Model Architect

NVIDIA; California (MO); On-site; USD 152,000 - 242,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA is seeking a hands-on Applied Engineer to advance robotics foundation model training at scale, working with researchers and engineers to move models from experimentation to production. You will optimize end-to-end training workflows for multimodal robotics models, build reference architectures, and scale workloads across multi-GPU systems, improving throughput and efficiency.

Collaborative cross-team work across researchers, engineers and customers aims to influence product direction and

Qualifications

• MS, PhD or equivalent 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 (e.g., VLMs, VLAs, World 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 (e.g., decoding, tokenization, batching)
• 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 that accelerate experimentation, benchmarking, and model improvement of NVIDIA’s Robotics Open model platforms like Cosmos and GR00T.
• Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems, improving utilization, throughput, and memory efficiency.
• Identify and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets (video, sensor data, trajectories).
• Collaborate with NVIDIA product and engineering teams to provide feedback that shapes future Physical AI platforms

Skills

Deep learning

Distributed computing

Large-scale model training

Multimodal training

PyTorch

NVIDIA NeMo

JAX

Hugging Face Transformers

Nsight Systems

Nsight Compute

PyTorch Profiler

Education

MS/PhD or equivalent

Tools

PyTorch

NVIDIA NeMo

JAX

Hugging Face Transformers

Nsight Systems

Nsight Compute

PyTorch Profiler

Job description

NVIDIA is seeking a hands-on Applied Engineer to advance robotics foundation model training at scale, working with researchers and engineers to move models from experimentation to production. You will optimize end-to-end training workflows for multimodal robotics models, build reference architectures, and scale workloads across multi-GPU systems, improving throughput and efficiency.

Collaborative cross-team work across researchers, engineers and customers aims to influence product direction and