Senior Solutions Architect, Robotics Foundation Model Training

About the role

We are building a team of innovators to help partners develop and adopt the next generation of Physical AI, spanning data generation, large-scale multimodal model training, robotics simulation and deployment.

Responsibilities

• Engage with Researchers and ML engineers to 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.

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 (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.

Qualifications

Experience across the AI model lifecycle, including pre-training, supervised fine-tuning, RL or other post-training methods, evaluation, and model optimization.

Skills

• Familiarity with NVIDIA AI and robotics platforms (e.g., Cosmos, GR00T, NeMo, Isaac Sim, 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.

Benefits

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits.

Pay

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

Schedule

N/A