3D Reconstruction Research Scientist

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 3D Reconstruction Research Scientist to join our Research Team in London.

Our team researches and builds the 3D reconstruction technology behind Niantic Spatial's platform.

These pipelines turn casually captured or domain specific imagery into high-fidelity explorable 3D reconstructions.

This entails anything from Structure-from-Motion through to 3D Gaussian Splatting, feed-forward models, and generative models.

Our team shipped 360 splatting to consumers and enterprise customers, developed the splatting tech that runs on phones, shipped multiple POCs to enterprise customers, and have innovated on countless AR technologies.

Our team has published at top venues (CVPR, ICCV, ECCV, SIGGRAPH); more often than not, our papers end up in production.

We're hiring a researcher to work at both ends of that pipeline.

You will implement and iterate on the state-of-the-art for both academic publishing and production, regularly working on prototypes with a short turnaround into customer-facing production.

You will work alongside world-class researchers in computer vision, machine learning, graphics, and robotics to develop new approaches for large-scale spatial understanding, localization, reconstruction, scene understanding, and geospatial foundation models.

WHAT YOU'LL DO Conduct original research in computer vision, machine learning, and spatial AI.

Design, implement, and evaluate novel algorithms for: 3D reconstruction Neural scene representations 3D Gaussian Splatting Geometric deep learning Structure-from-Motion (Sf M) Meshing Feed-forward models Generative models Spatial foundation models Collaborate with research scientists and engineers to transition promising ideas into future products.

Develop large-scale experiments and benchmarking frameworks.

Work directly with the realities of production with real capture conditions, real hardware constraints, and real customers.

Publish research findings at leading conferences such as CVPR, ICCV, ECCV, or SIGGRAPH.

WHAT YOU'LL BRING Ph D in Computer Vision, Robotics, Machine Learning, Computer Science, Mathematics, Physics, or a closely related field.

Exceptionally strong candidates with equivalent experience in industry would be considered.

A strong research track record in 3D computer vision: reconstruction, depth estimation, novel view synthesis, Sf M, SLAM, or closely related areas.

Strong publication record at top-tier conferences such as CVPR, ICCV, ECCV, SIGGRAPH, Neur IPS, etc.

Strong programming skills in Python and experience in Py Torch.

Evidence that you can carry an idea from research into something real; that may be a shipped feature, a production pipeline, a widely used open-source release, or similar.

Ability to independently drive research projects from idea generation through experimentation and publication.

NICE TO HAVE Experience with classical reconstruction tooling (COLMAP or similar).

C++ or CUDA experience, and a feel for performance and on-device constraints.

Experience with traditional 3D techniques - meshing, texturing, or bringing modern geometry or generative representations into native 3D.

COMPENSATION