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, reference architectures, and agentic workflows to accelerate experimentation and model improvement.
• Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems.
• Improve hardware utilization, throughput, and memory efficiency for large-scale training processes.
• Identify and eliminate data pipeline bottlenecks in storage, networking, and preprocessing for multimodal datasets.
• Develop high-throughput data pipelines for processing video, sensor data, and trajectories.
• Provide technical feedback to product teams to shape the future of 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 like VLMs, VLAs, and World Models.
• Experience across the AI lifecycle including pre-training, supervised fine-tuning, RL, evaluation, and model optimization.
• Expertise in distributed training techniques such as data/model/pipeline parallelism on multi-GPU or multi-node systems.
• Proficiency with multimodal training frameworks like 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 effectively across research, engineering, and executive teams.