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Matthijs Douze

Research Scientist, Fundamental AI Research (FAIR), since November 2015, Meta

  • Research Scientist, Fundamental AI Research (FAIR), since November 2015, Meta14

Matthijs Douze is a research scientist at Meta's Fundamental AI Research (FAIR) lab, where Douze has worked since November 2015 on large-scale indexing, machine learning with graphs, and similarity search for images and videos. The Faiss README lists Douze among the library's main authors as the person who implemented most of the CPU version, and Douze is first author of the 2024 paper "The Faiss library". From 2005 to 2015 Douze worked in the LEAR team at INRIA on image and vector indexing and video search.123

At a glance

Current role
Research Scientist, Fundamental AI Research (FAIR), since November 2015
Company
Meta
Sector
Company
Work listed
2
Sources cited
5
Profile reviewed

Career

From 2005 to 2015 Douze worked in the LEAR team at INRIA on image indexing, large-scale vector indexing, event recognition in videos and similar-video search. Between 2010 and 2015 Douze also managed Kinovis, a 3D motion capture studio at INRIA, and developed geometric algorithms for constructive solid geometry.1

Douze joined Facebook AI Research (FAIR) as a research scientist in November 2015, working on large-scale indexing, machine learning with graphs, and similarity search on images and videos.1

Faiss and research

The Faiss repository, created in February 2017 under the MIT license, credits Hervé Jégou with initiating the project, Douze with implementing most of the CPU Faiss, and Jeff Johnson with the GPU Faiss. Douze co-authored "Billion-scale similarity search with GPUs" (2017) and was first author of "The Faiss library" (2024).253

Douze's other FAIR publications include work on deep clustering for unsupervised visual features (2018), the Stable Signature method for watermarking latent diffusion models (2023), and research showing that watermarking can make language models "radioactive" (2024).1

Work and contributions

  • Faiss Open-source code

    Listed in the Faiss README as having implemented most of the CPU Faiss, and first author of "The Faiss library" (2024).23

  • Billion-scale similarity search with GPUs Published research

    Co-author, with Jeff Johnson and Hervé Jégou, of the 2017 paper on GPU similarity search that the Faiss README asks users to cite for GPU Faiss.52

Timeline

  1. Joined the LEAR team at INRIA, working on image and large-scale vector indexing.1
  2. Joined Facebook AI Research (FAIR) as a research scientist.1
  3. Co-authored "Billion-scale similarity search with GPUs" as the Faiss repository was opened.52
  4. Co-authored "Deep Clustering for Unsupervised Learning of Visual Features".1
  5. Co-authored "The Stable Signature", on watermarking latent diffusion models.1
  6. Posted "The Faiss library" as first author.3

Other documented roles

Roles the sources document that are not confirmed as current.

  • Researcher in the LEAR team (2005–2015); manager of the Kinovis motion capture studio (2010–2015), INRIA1

Where they work

  • Meta 106 people in the wiki

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References

5 sources · profile reviewed Oct 22, 2026 · data updated Oct 22, 2026

  1. Matthijs Douze — AI at Meta profileMeta · Official page · accessed Oct 22, 2026
  2. facebookresearch/faiss README (Authors and Reference) and license (MIT)facebookresearch (GitHub repository) · Repository · accessed Oct 22, 2026
  3. The Faiss library (arXiv 2401.08281)arXiv · Paper · published Jan 16, 2024 · accessed Oct 22, 2026
  4. mdouze (Matthijs Douze) on GitHubGitHub · Official page · accessed Oct 22, 2026
  5. Billion-scale similarity search with GPUs (arXiv 1702.08734)arXiv · Paper · published Feb 28, 2017 · accessed Oct 22, 2026