Research Scientist, Spatial AI & Perception
- Design and implement real-time SLAM and perception-based state estimation for a mobile humanoid or specialized data collection devices operating in unstructured, dynamic environments
- Build offline 3D reconstruction pipelines (multi-view geometry, SfM/MVS, neural reconstruction, depth/pose fusion) that generate geometric scene structure to inform and supervise large VLM/VLA training
- Pioneer research integrating large VLA and VLM models with 3D spatial perception to enable semantic, language-grounded scene reasoning.
- Bridge classical geometric methods and learned approaches - knowing when to use optimization-based estimation versus learned representations, and how to combine them.
- Write high-quality, maintainable C++ and Python code that fits into a large production codebase.
- PhD in Robotics, Computer Vision, Machine Learning, Computer Science, or related fields (or equivalent research experience).
- Prior experience building, and deploying SLAM, visual odometry, or 3D reconstruction systems for robots or autonomous vehicles.
- Strong background in one or more of the following:
- Real-time SLAM, visual-inertial odometry, and state estimation
- 3D reconstruction (SfM, MVS, multi-view geometry, neural/implicit reconstruction)
- Probabilistic state estimation and sensor fusion (factor graphs, filtering, optimization on manifolds)
- Spatial representations, grounding language/vision into 3D geometry, geometric foundation models
- Solid foundation in the math underlying geometric perception (Lie groups, nonlinear optimization, multi-view geometry).
- Strong analytical and debugging skills; ability to write reliable, well-structured research code in C++ and Python.
- Experience with modern ML frameworks (PyTorch, JAX) and an understanding of how perception outputs feed large-scale model training.
- Experience building reconstruction or data pipelines that produce training data for large vision or VLA models.
- Familiarity with VLA / large behavior models and how spatial grounding improves manipulation and long-horizon behavior.
- Publications in top-tier computer vision, ML, or robotics conferences (e.g., CVPR, ICCV, ECCV, RSS, ICRA, CoRL).
- Direct access to the world’s most advanced humanoid robot: test your models on hardware quickly and often.
- A collaborative, inclusive team that values diverse perspectives and identities.
- The opportunity to do applied spatial perception and VLA research with real-world impact.
- A mission-focused environment where your work will define the future of general-purpose humanoids.
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