Senior Solutions Architect, Robotics Foundation Model Training

• 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!
• 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
• 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.
• 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
• You will also be eligible for equity and benefits .
• Applications for this job will be accepted at least until August 31, 2026.
• This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.

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