3D Reconstruction Research Scientist

Niantic, Inc.; City of Westminster; On-site; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Annual bonus

Equity

Comprehensive benefits

Job summary

Niantic, Inc. in London seeks a 3D Reconstruction Research Scientist to advance spatial AI for large-scale understanding. You will research 3D reconstruction, neural scene representations, and geometric deep learning, collaborating with researchers and engineers to bring ideas toward products.

You will lead experiments, publish at CVPR/ICCV/ECCV/SIGGRAPH, and work under production realities with real capture data and hardware constraints. A PhD and strong 3D CV publication record are required.

Qualifications

• Conduct original research in computer vision, machine learning, and spatial AI.
• Design and evaluate novel algorithms for 3D reconstruction, SfM, and meshing.
• Publish findings at top conferences (CVPR, ICCV, ECCV, SIGGRAPH).

Responsibilities

• Collaborate with researchers and engineers to transition ideas into products.
• Develop large-scale experiments and benchmarking frameworks.
• Work with real capture conditions and production constraints.
• Publish research findings at leading conferences.

Skills

Python

PyTorch

3D computer vision

Research publication

Machine Learning

SLAM / SfM

Education

PhD in Computer Vision, Robotics, ML, CS, Math, Physics, or related field

Tools

COLMAP

C++

CUDA

Job description

We are looking for 3D Reconstruction Research Scientist to join our Research Team in London., 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.

• 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 (SfM)
  • 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.
• PhD 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, SfM, SLAM, or closely related areas.
• Strong publication record at top-tier conferences such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, etc.
• Strong programming skills in Python and experience in PyTorch.
• 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.

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.

Base salary: Competitive market rate. Compensation also includes an annual bonus, equity, and a comprehensive benefits package.