Building foundational robotics models to revitalize domestic manufacturing and defense production.
Staff Machine Learning Engineer, VLA Training
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We are a small, well-funded team founded this year to solve a deceptively simple problem: American factories need to move faster, and the only way to do that is to teach machines how to see, reason, and act in the physical world. Our approach centers on vision-language-action models that let robotic systems interpret unstructured environments and execute complex assembly tasks without hand-coded paths. This is not lab research for a paper — it is engineering for the factory floor, where latency, reliability, and generalization are non-negotiable.
What they're looking for
- 6+ years building and training large neural networks in PyTorch, with at least 2 years focused on multimodal architectures that combine vision, language, or action modalities.
- Deep understanding of VLA or vision-language model training pipelines, including data curation, distributed training, fine-tuning strategies, and real-world evaluation.
- Comfortable working end-to-end: you can debug a CUDA memory leak, refactor a dataset loader, and reason about robot kinematics in the same afternoon.
- Experience in a high-autonomy environment where you defined technical direction for a small group, not just executed a roadmap handed to you.
- Must be based in or willing to relocate to New York; this role works alongside hardware daily.