3D Computer Vision Research Engineer - World Map

Posted 2 Days Ago
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London, Greater London, England, GBR
Hybrid
Entry level
Artificial Intelligence • Computer Vision • Machine Learning • Software
The Role
Develop algorithms and production systems for world-scale geospatial registration, 3D geometry, correspondence, mapping, and learned 3D reasoning. Build datasets, pipelines, benchmarks, and evaluation methods using imagery, point clouds, LiDAR, GPS, and geographic data. Apply computer vision, machine learning, transformers, and geometric optimization to improve a living world map, while collaborating across research, engineering, and product teams and communicating results through technical documentation and presentations.
Summary Generated by Built In
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

You'll help build the geometric foundation of the next-generation world map — joining our World Map Research Engineering team (Geospatial Alignment) as a Research Engineer or Senior Research Engineer. This is an end-to-end applied research role that spans from strong engineering and data-systems work to R&D in 3D deep learning, transformers, and foundation models. You'll develop new algorithms, produce the research data and evaluation assets needed to advance them, and engineer the resulting capabilities into dependable production systems.

The World Map team works with heterogeneous captures from phones and handheld devices, aerial imagery, LiDAR, and 360 cameras, as well as external geographic and elevation data. The central challenge is to harmonize these data sources, quantify uncertainty, and maintain consistency as the map evolves.

You'll work at the intersection of cutting-edge research and scalable engineering — developing new approaches to geospatial registration, correspondence, and geometric optimization, then productionizing these technologies to directly improve Niantic Spatial's world model.

What You'll Do

  • Geospatial Registration — Develop and advance algorithms for world-scale georeferencing and registration across imagery, 3D reconstructions, point clouds, and geographic reference data.

  • 3D Geometry & Correspondence — Build and improve methods spanning multi-view geometry, SfM, SLAM, feature matching, learned correspondence, bundle adjustment, point-cloud registration, and ICP.

  • Data & Evaluation — Create the data and evaluation flywheel for world-scale mapping — design and maintain large-scale datasets, pipelines, benchmarks, and evaluation methodologies that expose hard failure modes, accelerate research iteration, and support production.

  • Learning-Based 3D Reasoning — Explore and develop learning-based approaches for geospatial understanding, combining transformers and 3D foundation models with geometric priors and uncertainty modeling.

  • Production Robustness — Design robust solutions for challenging real-world conditions: sparse or noisy GPS, changing appearance, inconsistent reconstruction quality, large geographic extents, and imperfect reference maps.

  • Cross-Functional Collaboration — Partner with leading researchers in visual localization, 3D computer vision, and mapping — as well as engineering and product teams — to identify high-impact problems and turn research breakthroughs into end-to-end production systems.

  • Research Communication — Share results clearly through technical writing, design reviews, and internal presentations; contribute to publications and open research where appropriate.

What You'll Bring
  • Bachelor's or Master's degree in Computer Science, Robotics, Machine Learning, Photogrammetry, or a closely related field — or equivalent research or industry experience.

  • Strong grounding in 3D computer vision, machine learning, robotics, photogrammetry, or a closely related area.

  • Demonstrated experience with 3D geometry and several relevant areas — multi-view reconstruction, SfM, SLAM, camera pose estimation, point-cloud registration, image-based localization, georeferencing, or sensor fusion.

  • The ability to formulate ambiguous real-world problems, choose the right mathematical or learning-based tools, and evaluate solutions rigorously.

  • Strong Python skills and experience with modern ML and scientific-computing libraries (PyTorch, NumPy, SciPy).

  • Strong software engineering skills — you can turn research prototypes into reliable, scalable, and maintainable systems.

  • A track record of taking an algorithm from research idea and experimental prototype through evaluation, benchmarking, and deployment on real-world data.

  • Clear written and verbal communication; able to collaborate effectively across research and engineering disciplines.

Nice to Have
  • PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field — not required, but valued.

  • A strong publication record or active involvement in the research community at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, 3DV, ICRA, or RSS.

  • Experience with learning-based approaches for visual or 3D understanding — transformers, point transformers, foundation models, or methods combining learned representations with explicit geometric priors.

  • Experience with aerial or satellite imagery, GNSS/GPS, 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. We welcome applications from candidates who require visa sponsorship.

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.

Skills Required

  • Bachelor's or Master's degree in Computer Science, Robotics, Machine Learning, Photogrammetry, or a closely related field, or equivalent research or industry experience.
  • Strong grounding in 3D computer vision, machine learning, robotics, photogrammetry, or a closely related area.
  • Experience with 3D geometry and relevant areas such as multi-view reconstruction, Structure from Motion, SLAM, camera pose estimation, point-cloud registration, image-based localization, georeferencing, or sensor fusion.
  • Ability to formulate ambiguous real-world problems, select appropriate mathematical or learning-based tools, and evaluate solutions rigorously.
  • Strong Python skills and experience with PyTorch, NumPy, SciPy, and modern machine-learning or scientific-computing libraries.
  • Strong software engineering skills for developing reliable, scalable, and maintainable systems from research prototypes.
  • Track record of taking algorithms from research ideas and experimental prototypes through evaluation, benchmarking, and deployment on real-world data.
  • Clear written and verbal communication and ability to collaborate across research and engineering disciplines.
  • PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field.
  • Strong publication record or active research-community involvement at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, 3DV, ICRA, or RSS.
  • Experience with learning-based visual or 3D understanding, including transformers, point transformers, foundation models, or learned representations with geometric priors.
  • Experience with aerial or satellite imagery, GNSS/GPS, geodesy, mapping systems, or large-scale spatial data.
  • Evidence of technical leadership, mentorship, or successful cross-functional delivery.
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The Company
182 Employees
Year Founded: 2025

What We Do

Niantic Spatial is building a living model of the world for machines, developing a geospatial AI model to understand and digitally map the physical world through spatial foundation and large geospatial models.

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