Research Scientist

About Niantic Spatial

At Niantic Spatial, we're building the future of physical AI. Powered by a proprietary database of over 30 billion posed images, our groundbreaking mapping technology unlocks a new dimension of interaction and spatial intelligence that helps both humans and machines better understand, represent, navigate, and engage with the real environment.

Our reconstruction technology captures environments with geometric accuracy and extreme detail from any standard camera, and our Visual Positioning System delivers precise positioning almost anywhere in the world. We serve customers across robotics, the public sector, and energy and industrial markets - building for the 80% of economic activity that takes place beyond our screens.

About the Role

Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI.

Niantic Spatial's R&D Team in London is seeking a Research Scientist to push the frontiers of Spatial AI, advancing our Large Geospatial Model as a dedicated member of the Embodied AI group. You will invent and develop algorithms and systems that sit at the cross-section of computer vision, machine learning, AI, graphics, and robotics.

We're passionate about discovery, so we're looking for scientists who want to learn, publish, teach, and deploy every new invention.

What You'll Do

• Develop New Spatial Representations - Develop new spatial representations that embodied AI can learn from, train in, and act on.

• Advance Real-to-Sim Pipelines - Advance real-to-sim pipelines connecting real-world observations, 3D reconstruction, simulation, and robotic policy learning.

• Solve Emerging Spatial Problems - Solve emerging problems that relate to spatial intelligence, 3D scene representation, training data, model efficiency, and interactive systems.

• Take Capabilities Through to Evidence - Take new capabilities through to evidence: what changed for the policy, not only what changed in the reconstruction.

• Build Prototypes - Build proof of concept prototypes, initially for internal use, where some will need to run at production scale on customer data.