Position OverviewAs a Research Engineer on the AI Research team, you will own the end-to-end loop of Embodied AI. You will leverage our massive, "in-the-wild" multimodal datasets to develop models that don't just move - they generalize, self-improve, and redefine what is possible in unstructured human environments.
What You'll Do- Advance the Frontier: Design and implement state-of-the-art robot learning algorithms (RL, Imitation Learning, Diffusion) for complex, high-DOF manipulation tasks.
- VLA & Video Generation: Architect and scale multi-modal foundation models (VLMs/VLAs) and leverage generative video architectures to enhance world-modeling and robot foresight.
- End-to-End Ownership: Own the full research-to-production pipeline: from task definition and data curation to large-scale training and real-time deployment on physical hardware.
- Physical-World Evaluation: Move beyond the benchmark. You will evaluate and iterate on models using our expanding robot fleet, ensuring commercial-grade performance in dynamic customer environments.
- Collaborative Leadership: Work at the intersection of AI, Hardware, and Teleop teams to enhance the "Intelligence-per-Watt" of our robotic systems.
What You'll Bring- Research Excellence: A PhD (preferred) or Master's in CS/Robotics with a significant publication record in top-tier conferences (RSS, CoRL, ICRA, NeurIPS, CVPR, or ICLR).
- 5+ Years of Mastery: Deep experience in AI and robotics research, specifically in deploying deep learning models onto physical robots (not just simulation).
- Generative AI Expertise: Hands-on experience with Vision-Language-Action (VLA) models, Video Generation, or large-scale transformer architectures.
- Technical Stack: Mastery of Python and PyTorch (or JAX). Familiarity with simulation stacks like MuJoCo, Isaac, or Drake.
- High-Quality Code: The ability to write maintainable, production-ready research code that scales across GPU clusters.
Bonus Points For- Experience with founding-stage startups or leading small, elite research teams.
- Deep understanding of classical robotics (controls, state estimation) to complement learning-based approaches.
- Experience profiling and optimizing foundation models for low-latency inference on edge devices.
Don't let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you're passionate about the intersection of geometry and robotics, we want to hear from you-even if you don't check every box.
