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.,

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.
Collaborate Across Research and Product - Collaborate closely with researchers, engineers, and product partners to bring novel models to production.
Publish Your Work - Publish and present your work.

A PhD in computer vision, robotics, graphics, machine learning, or a related field, or equivalent research experience, with a publication record at top venues.
Substantial expertise in robotics or embodied AI, including experience in sim-to-real transfer, domain randomization, policy evaluation, and simulation-based learning.
Deep experience with 3D scene representations: Gaussian splatting, neural fields, meshes, point clouds, or implicit representations.
You have applied generative or diffusion models to 3D data, such as scene completion, inpainting, relighting, or novel view synthesis from sparse capture.
You have worked on 3D semantics, open-vocabulary scene understanding, or scene graphs.
Proficiency in Python and Deep Learning libraries such as PyTorch.
GPU or graphics programming experience, such as custom CUDA kernels, shaders, or differentiable rendering.
A track record of contributing to widely used open-source research codebases.
Proficiency in C++.

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.

1 Niantic, Inc. jobs in England & Wales