Senior Solutions Architect, Robotics Foundation Model Training

What you'll do

• Architect and optimize end-to-end training workflows for robotics foundation models like World Models and VLAs.
• Build proof-of-concepts and agentic workflows to accelerate experimentation and benchmarking for robotics platforms.
• Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems.
• Improve training throughput and memory efficiency by optimizing distributed training techniques and sharding.
• Identify and eliminate bottlenecks in data pipelines for multimodal datasets including video and sensor data.
• Provide technical feedback to product and engineering teams to shape future Physical AI platforms.

What we're looking for

• MS, PhD, or equivalent experience in Computer Science, AI, Electrical/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, evaluation, and model optimization.
• Strong expertise in distributed training techniques on multi-GPU or multi-node systems.
• Expertise with multimodal training frameworks such as PyTorch, NVIDIA NeMo, JAX, or Hugging Face Transformers.
• Experience building high-throughput data pipelines for large-scale training, including storage, networking, and preprocessing.
• Strong communication skills to collaborate across Researchers, Engineers, and executives.