Niantic Spatial · London
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 can't yet reason about real places well enough for robots, field crews and AI agents to rely on it, and closing that gap will change how factories, power grids, cities and defense operations run. That's one of the biggest unsolved problems in AI, and it's the one we're solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That's why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world's most widely used maps. Now we're building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. ABOUT THE ROLE The Large Geospatial Model is Niantic Spatial's foundation model of the physical world. It learns from years of real-world captures of millions of places, and people, robots and AI agents use it to find out where they are, what is around them and what has changed. Each step up in scale has given the model capabilities it did not have before. The next problems are some of the hardest in AI: filling in the parts of a place no camera saw while marking what was inferred, measuring what changed between visits, and predicting what will change next. Customers already use the model in production. Energy companies use it to guide field crews to equipment on large industrial sites, defense partners use it for pos…
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