The RoleWhile our existing perception division handles classical state estimation and VIO, this role is dedicated to the next generation of spatial and temporal intelligence. We are hiring a Research Scientist to architect and train proprietary foundation models from scratch.
Your core mandate is twofold: building our in-house equivalents to cutting-edge 3D reconstruction architectures, and developing highly robust optical flow models tailored for the chaotic domain of egocentric vision. Beyond these core pillars, you will serve as a lead problem-solver for emergent perception challenges as our hardware and downstream robotics needs evolve.
To achieve this,
we can provide a massive, continuous stream of high-quality, proprietary ground-truth data captured by our infrastructure. You will use this data advantage to train networks that surpass current public baselines, owning the complete spatial-temporal perception loop for our data engine.
What You'll Work OnArchitecting Proprietary Spatial Models- Zero-to-One Model Development: Design, implement, and train state-of-the-art feed-forward network and per-scene differential optimization architectures for 3D geometry extraction.
- Large-Scale Distributed Training: Scale multi-view ML architectures across multi-GPU clusters to handle massive, multi-modal spatial datasets.
- Loss & Architecture Innovation: Push the boundaries of current paradigms by developing novel loss functions and attention mechanisms tailored to our specific data distributions.
Egocentric Optical Flow & Temporal Dynamics- Egocentric Motion Modeling: Build and train custom optical flow architectures capable of handling the extreme motion blur, rapid rotations, and sudden occlusions inherent in head-mounted or robot-mounted cameras.
- Dynamic Scene Understanding: Use your flow models to segment dynamic actors, track objects through complex manipulations, and provide motion regularization for downstream action-conditioned world models.
Emergent Perception R&D- Rapid Prototyping: Tackle novel, unmapped AI challenges as they arise. You will rapidly prototype and deploy new models for tasks spanning tracking, segmentation, multi-modal sensor fusion, and beyond.
- Agile Problem Solving: Pivot to resolve sudden algorithmic bottlenecks in the data engine, adapting the latest research to unblock new product capabilities or hardware integrations.
Next-Level Dense Reconstruction- Neural Rendering Integration: Connect the outputs of your foundational models into highly optimized, large-scale dense reconstruction pipelines (3D Gaussian Splatting, NeRFs) to generate photorealistic environments.
Who You AreRequired Background- Deep expertise in Deep Learning, 3D Computer Vision, and Temporal/Video Modeling.
- Proven experience training large-scale vision models from scratch, not just running inference or fine-tuning.
- Strong theoretical and practical understanding of modern feed-forward 3D networks and dense motion estimation.
- Mastery of PyTorch and deep learning scaling frameworks.
- Experience handling and curating massive, multi-terabyte image and video datasets for training.
- Comfortable operating in a fast-paced environment where priorities can shift rapidly to capitalize on new research or hardware capabilities.
- Warning: Research Scientist positions require hyper-specific expertise. Please limit your applications to one research role. Applying to multiple Research Scientist positions suggests a lack of focus and may result in the rejection of all submissions. You may, however, apply to other non-research roles alongside your research application.
Strong Signals:- First-author publications in top-tier venues (CVPR, ICCV, ECCV, NeurIPS) focusing on 3D deep learning, optical flow, video generation, or spatial transformers.
- Specific experience working with egocentric video datasets (e.g., Ego4D, Ego-Exo4D) and solving the unique optimization challenges they present.
- Experience writing custom CUDA kernels to accelerate 3D operations, ray marching, or correlation volume computations.
Why This Role?- The Data Advantage: You will have access to a scale and quality of proprietary spatial and temporal ground truth that most academic researchers only dream of.
- Pure R&D & Model Ownership: You are not maintaining legacy systems; you are given a blank slate and the compute resources to build the state-of-the-art.
- High Impact: The spatial priors and motion models you architect will directly define how the next generation of embodied AI agents perceive and move through the physical world.
A Note on ApplyingStudies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%. If you don't check every box above but believe you can do the job, we encourage you to apply - we're looking for capability and trajectory, not a perfect checklist match.
