Research Scientist- SLAM & VIO

The Role

While 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 On

Architecting 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.

What You Bring

Required 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.

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.

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.