---
title: '3D Computer Vision Research Engineer, Geospatial Visual Localization at Niantic Spatial'
canonical: 'https://feeny.ai/job/3d-computer-vision-research-engineer-geospatial-visual-localization-niantic-1ff9cc2q6npq'
type: 'job'
last_seen: '2026-11-02'
---

# 3D Computer Vision Research Engineer, Geospatial Visual Localization at Niantic Spatial

- **Company:** Niantic Spatial
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-29
- **Last confirmed live:** 2026-11-02
- **Apply:** https://jobs.ashbyhq.com/niantic-spatial/c1864d09-d969-4b86-87aa-67c2783880a3

## 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 You'll Do

- Design, implement, and evaluate novel visual localization algorithms, combining 3D vision and deep learning.
- Train large models on multi-modal data at scale, and build the benchmarks that measure their accuracy and robustness.
- Develop robust solutions for difficult real-world conditions: noisy or denied GPS, changing appearance, varying capture quality, large geographic extents, and imperfect reference maps.
- Work side-by-side with leading researchers in visual localization, 3D computer vision, and mapping, as well as engineering and product teams, to identify the highest-impact problems and turn research into end-to-end solutions that operate at production scale.
- Communicate results clearly through technical writing, design reviews, internal presentations, and publications.

## What You'll Bring

- Strong grounding in 3D computer vision, deep learning, robotics, photogrammetry, or a closely related field.
- Demonstrated experience with multi-view geometry and related problems, such as camera pose estimation, monocular depth estimation, SfM, SLAM, georeferencing, point-cloud registration, or sensor fusion.
- Knowledge of transformer models for 3D vision, such as vision foundation models, feed-forward 3D reconstruction, or multi-view and multi-modal attention.
- Knowledge of geospatial data and coordinate reference systems.
- Ability to formulate ambiguous real-world problems, choose appropriate mathematical or learning-based tools, and evaluate solutions rigorously.
- Strong Python skills and experience training deep learning models at scale, with libraries such as PyTorch, NumPy, and SciPy.
- Strong software engineering skills, with the ability to turn research prototypes into reliable, scalable, and maintainable systems.
- Experience taking an algorithm from research idea and experimental prototype through evaluation, benchmarking, and deployment on real-world data.
- Clear written and verbal communication and the ability to collaborate across research and engineering disciplines.

## Nice to Have

- A PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field, or equivalent research or industry experience. A PhD is not required.
- A strong publication record or substantial active involvement in the research community, especially at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, SIGGRAPH Asia, 3DV, ICRA, RSS, or related conferences and journals.
- Experience with aerial or satellite imagery, GNSS/GPS, IMUs, LiDAR data, geodesy, mapping systems, or large-scale spatial data.
- Evidence of technical leadership, mentorship, or successful cross-functional delivery.

## Compensation & Benefits

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

Location & Work Model This role is based in our London office, with three days per week in office.

Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for.

## Equal Opportunity

Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process.

Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

## About Niantic Spatial

## Company Overview
- **One-liner**: Niantic Spatial builds real-world foundation models and spatial intelligence systems that enable machines to reconstruct, localize within, and understand the physical world at global scale.
- **Entity Type**: Private (startup, spun out of Niantic in 2025)
- **Headquarters**: San Francisco, CA, USA (with offices in Sunnyvale, Bellevue, London, and Tokyo)
- **Founded**: 2025 (as Niantic Spatial; roots trace back to Keyhole, founded 2001, and Niantic Labs, 2011)
- **Founders**: John Hanke (Founder & Executive Chairman); Thomas Gewecke (CEO), Brian McClendon (CTO), Inhi Cho Suh (President & COO)

## Core Business
- **Primary industry/industries**: Geospatial AI, physical AI infrastructure, spatial computing, robotics, defense, energy, construction, logistics
- **Target customers**: B2B – enterprises in robotics, oil & gas, defense, large venues, construction, logistics; public sector; also powers consumer apps via Scaniverse
- **Mission or purpose statement**: “Build a living model of the world that people and machines can talk to.”

## Products & Services
- **Large Geospatial Model (LGM)** – The core engine trained on one of the largest datasets of real‑world spatial imagery. It powers reconstruction, localization, and understanding capabilities at global scale. Accessed via Scaniverse and licensed to partners. (Model/API)
- **Reconstruct** – High‑fidelity, geo‑referenced 3D models (meshes and Gaussian splats) created from any standard camera, 360° camera, drone, LiDAR, or satellite imagery. (Technology/Service)
- **Localize (Visual Positioning System)** – Precise global positioning and orientation for humans and machines, even in GPS‑degraded or denied environments (indoors, outdoors, anywhere). (Technology/API)
- **Understand** – Spatial foundation models that give AI the ability to reason about the physical world: semantic understanding at every 3D point, queryable in natural language. (Technology/API)

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Over 300 million user‑contributed scans; 30+ billion posed images from real‑world locations; 140+ patents filed or pending
- **Notable Investors/Partners**: Backed by Niantic (the Pokémon GO creator) and its internal resources; no external funding round publicly announced. Partnerships in defense, energy, construction, logistics, and large venues.
- **Growth Signals**: Formed as an independent company in 2025; actively hiring for senior engineering roles (Computer Vision, Tech Lead, AI Data Operations); expanding from consumer AR into enterprise and government sectors.

## Competitive Advantages
- **Deep heritage**: Founders built Keyhole (acquired by Google, became Google Earth/Maps) and later created the world’s first global Visual Positioning System at Niantic.
- **Unique data moat**: 300M+ crowdsourced scans and 30B+ posed images provide a proprietary dataset of real‑world spatial imagery that competitors would find extremely difficult to replicate.
- **Planetary‑scale deployment**: Proven ability to operate at global scale (Pokémon GO had 500M+ users); technology already battle‑tested in demanding consumer and enterprise environments.
- **Patent portfolio**: 140+ patents covering spatial reconstruction, localization, and understanding.

## Strategic Focus
- Continue building spatial foundation models that can reason about the physical world in natural language
- Expand vertical markets: robotics (warehouse, delivery, autonomous), defense (DDIL environments), oil & gas (facility digital twins), large venues, and construction
- Drive adoption of the Large Geospatial Model (LGM) as the standard for physical AI reasoning
- Integrate consumer‑facing Scaniverse as the front door to the LGM, simultaneously feeding the model with new user scans

## Why Work Here
- **Culture & mission**: High‑impact startup environment focused on a mission to create a living 3D model of the world; employees describe it as fast‑moving and collaborative
- **Work model**: Hybrid – blends in‑office collaboration with flexibility. Offices in San Francisco, Sunnyvale, Bellevue, London, and Tokyo.
- **Benefits**: 100% paid healthcare for employees, short‑/long‑term disability & life insurance (premiums covered), generous parental leave, flexible time off, 401(k) with company match, stock option grants (equity participation), and legal assistance (ARAG).
- **Engineering culture**: Deep technical challenges in computer vision, geospatial AI, large‑scale ML, and robotics; tight‑knit team of experts from Google Earth, Niantic, and the AR industry.
- **Growth trajectory**: Newly independent company with strong backing, ample opportunity to shape the product and technical direction.

## Sources
1. [nianticspatial.com – Homepage](https://www.nianticspatial.com/)
2. [nianticspatial.com – Careers](https://www.nianticspatial.com/careers)
3. [nianticspatial.com – About](https://www.nianticspatial.com/about)
4. [nianticspatial.com – About (alternate)](https://www.nianticspatial.com/en/about)

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