3D Computer Vision Research Engineer, Geospatial Visual Localization

Job description

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, or what changes over time. 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 We are looking for a Research Engineer to help develop our Geospatial Visual Localization technologies, which allows people and robots to accurately localize themselves anywhere on the planet. This is an end-to-end applied research role. You will develop new approaches to open problems alongside our researchers in 3D vision and machine learning, and publish your work at leading conferences. You will help build and train large models on large-scale multi-modal datasets, including our proprietary collection of 30 billion posed images and ship them into production, to be used by people and robots from all over the world. Our team works with heterogeneous captures from phones and other handheld devices, 360 cameras, vehicles, and drones, as well as aerial and satellite imagery, LiDAR, and external geographic data. Your focus will be to improve accuracy and robustness across urban and rural locations, cameras, seasons, weather, and capture quality. The system must remain reliable with noisy GPS, in GPS-denied environments, and with outdated reference maps. Each pose estimate comes with an uncertainty that our customers rely on, and keeping it well calibrated across these conditions is part of the work. What Yo…