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About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together,…
This engineer trains and evaluates PyTorch computer-vision models (feature matching, image retrieval, pose regression) behind Niantic Spatial's centimeter-level Visual Positioning System. Day to day: building data pipelines, running large-scale GPU training on a 30-billion-image corpus, benchmarking, and helping move models into production.
Senior Computer Vision Engineer on Niantic Spatial's Embodied AI team, turning 3D reconstruction and spatial-perception research into reliable real-to-sim pipelines for embodied AI customers — owning output quality, robustness, and GPU/distributed performance. Core stack: Python, C++, CV/ML, photogrammetry/SLAM, Gaussian splatting, cloud GPU workloads.
Senior Computer Vision Engineer on Niantic Spatial's Applied Algorithms team owns key parts of the Localization stack: building and hardening the Visual Positioning System (VPS) and 3D mapping pipelines that power a living geospatial world model. Day-to-day involves SfM and feed-forward model work, moving R&D to production, and GPU/CPU performance optimization in Python/C++ with PyTorch or JAX.
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