Senior Director of Product Management, Models and Data

<h2>About Niantic Spatial</h2><p style="min-height:1.5em">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.</p><p style="min-height:1.5em">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.</p><p style="min-height:1.5em">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.</p><p style="min-height:1.5em">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.</p><p style="min-height:1.5em"></p><h2>About the Role</h2><p style="min-height:1.5em">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.</p><p style="min-height:1.5em">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 positioning where GPS is jammed or unavailable, and robotics companies train robots in metric-scale copies of the real sites they will work in.</p><p style="min-height:1.5em">You will be the first product leader for the model and the data it learns from, reporting to the Chief Product Officer. You decide which capabilities it gains and in what order, the bar each release has to clear, and what data Niantic Spatial collects, licenses or partners for to get there. You set those priorities directly with the Chief Scientist and the London research team who train the model.</p><p style="min-height:1.5em">If you have shipped a frontier language model, most of the job will be familiar: evals, data mix and release decisions. The job differs in two ways. Every output of this model can be scored against a measured real place, so its evals rest on ground truth. And its data is collected in the physical world, so choosing what to capture next is a product decision with a real cost. Experience in 3D or mapping is welcome but not required.</p><p style="min-height:1.5em">By the end of your first year, research, engineering and the product leads for industrial, defense, robotics and platform customers work from one model roadmap that you own, each release ships against evals you wrote, and the model's newest capabilities are in customers' hands.</p><p style="min-height:1.5em"></p><h2>What You'll Do</h2><ul style="min-height:1.5em"><li><p style="min-height: