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Research Scientist Intern, Spatial Reasoning & Localization; PhD

Job in Redmond, King County, Washington, 98052, USA
Listing for: Meta
Apprenticeship/Internship position Listed on 2026-11-07
Job specializations:
  • Research/Development
    AI Business & Operations, Data Scientist, Research Scientist
Salary/Wage Range or Industry Benchmark: 39000 - 52000 USD Yearly USD 39000.00 52000.00 YEAR

Job Description & How to Apply Below

Research Scientist Intern, Spatial Reasoning & Localization (PhD)# Research Scientist Intern, Spatial Reasoning & Localization (PhD)
Redmond, WA+1 location

Artificial Intelligence+ 1 more Apply nowMeta Reality Labs brings together researchers and engineers to invent the future of visual computing.

Our teams develop technologies that enable people to capture, create, understand, and interact with immersive visual experiences across Meta’s products and AR/VR platforms. We work at the intersection of artificial intelligence, computer vision, multimodal language models, and maps.

Our research spans localization, vision-language models, scene text and landmark understanding, and spatial reasoning grounded in maps and floor plans, so that devices such as AI glasses can understand where people are, indoors and outdoors.

As a Research Scientist Intern, you will help advance the state of the art in a fast, lightweight VLM driven visual localizer that reasons over semantic and geometric evidence in images and structured map representations, estimating where the image was taken: the place, the floor, and the camera's position and heading.

You will collaborate with researchers and engineers to develop new models, representations, datasets, and evaluation methods—with opportunities to publish your work and influence future products used by people around the world.

---## Research Scientist Intern, Spatial Reasoning & Localization (PhD) Responsibilities
* Research, develop and prototype advanced technologies in the domain of Computer Vision, Multimodal Machine Learning, and Visual Localization, such as compact vision-language models that extract structured spatial evidence from images and localize them against structured map representations of indoor and outdoor environments.
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