Senior Solutions Architect, Robotics Foundation Model Training

NVIDIA • Santa Clara, CA

9d • $152,000 - $287,500

About The Position

NVIDIA is seeking a hands-on Applied Engineer with deep expertise in large-scale foundation model training and a strong background in robotics. The role works at the intersection of AI research, accelerated computing, and real-world applications, partnering with model builders to move robotics models from experimentation to production. The position collaborates across research, engineering, and customer teams and contributes to product direction and applied AI adoption.

The role focuses on architecting and optimizing end-to-end training workflows for robotics foundation models, including World Models, VLAs, and WAMs. It also involves developing proof-of-concepts, reference architectures, and agentic workflows for NVIDIA robotics model platforms such as Cosmos and GR00T; scaling training and reinforcement-learning workloads; improving training infrastructure and data pipelines; and providing feedback to product and engineering teams.

Requirements

• 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, such as VLMs, VLAs, or World Models; robotics experience is preferred.
• Experience across the AI model lifecycle, including pre-training, supervised fine-tuning, reinforcement learning or other post-training methods, evaluation, and model optimization.
• Strong expertise in distributed training techniques, including data, model, and pipeline parallelism, sharding, and checkpointing, 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 such as decoding, tokenization, and batching.
• Strong communication skills and the ability to collaborate effectively with researchers, engineers, and executives.

Nice To Haves

• Familiarity with NVIDIA AI and robotics platforms such as Cosmos, GR00T, NeMo, Isaac Sim, and Isaac Lab.
• Experience with robotics AI workloads, including reinforcement learning in simulation and synthetic data generation.
• Experience profiling and optimizing workloads using tools such as Nsight Systems, Nsight Compute, or PyTorch Profiler.
• Demonstrated impact improving training efficiency and scaling performance.
• Experience building agentic workflows for automated experimentation, model evaluation, data analysis, or research acceleration.

Responsibilities

• Engage with researchers and ML engineers to architect and optimize end-to-end training workflows for robotics foundation models, such as World Models, VLAs, and WAMs.
• Build proof-of-concepts, reference architectures, and agentic workflows to accelerate experimentation, benchmarking, and model improvement for NVIDIA robotics open model platforms such as 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, including video, sensor data, and trajectories.
• Collaborate with NVIDIA product and engineering teams to provide feedback that shapes future Physical AI platforms.

Benefits

• Equity
• Benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior