PeopleofSuperintelligence

Researcher

Dirk Weissenborn

Dirk Weissenborn is a machine-learning researcher whose work spans language understanding, computer vision and AI-designed medicines. Early papers addressed named-entity disambiguation and extractive question answering; Jack the Reader later brought question answering, natural language inference and link prediction into one framework. He went on to apply Transformers to images, object-centric learning and open-vocabulary detection. As of October 2026, he contributes to Inceptive's research on AI-designed CAR-T mRNA.

Now at
Inceptive
Works
36

Publication numbers from OpenAlex, as of 22 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.

Timeline

  1. 2015

    Joint noun and entity disambiguation

    In 2015, he coauthors a multi-objective optimization paper for joint noun and named-entity disambiguation.

  2. 2017

    FastQA for extractive question answering

    In 2017, he coauthors a neural-baseline paper for extractive question answering; FastQA achieves competitive performance against existing systems.

  3. 2018

    Jack the Reader

    In 2018, he coauthors Jack the Reader, a machine-reading framework that supports question answering, natural language inference and link prediction.

  4. 2019

    Axial attention for multidimensional data

    In 2019, he coauthors Axial Attention in Multidimensional Transformers, an autoregressive self-attention model for images and other data organized as high-dimensional tensors.

  5. 2020

    Slot Attention for object-centric learning

    In 2020, he coauthors Slot Attention, an object-centric module with exchangeable slots that bind to any object through a competitive procedure over multiple rounds of attention. The module generalizes to unseen scene compositions.

  6. December 2020

    Vision Transformer for image recognition

    In December 2020, he and Neil Houlsby present the Vision Transformer, which represents images as sequences of patches and uses learned position embeddings to encode image structure.

  7. 2022

    Open-vocabulary object detection

    In 2022, he coauthors Simple Open-Vocabulary Object Detection with Vision Transformers, which transfers large-scale image-text pretraining to object detection. The paper reports strong zero-shot text-conditioned and one-shot image-conditioned detection results.

  8. As of October 2026

    CAR-T mRNA research at Inceptive

    As of October 2026, he contributes to Inceptive's research on AI-designed CAR-T medicines. In June 2026, the team's mRNA designs match industry benchmarks. A September 2026 report he coauthors shows three fine-tuned designs outperforming an optimized industry sequence in animals, with the top design clearing nearly all measured B cells.

  1. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Dirk Weissenborn. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  2. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  3. Google has the highest combined h-index among AI organizationsIncludes Google. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Multi-Objective Optimization for the Joint Disambiguation of Nouns and Named Entities aclanthology.org. Checked 22 October 2026.
  2. Making Neural QA as Simple as Possible but not Simpler aclanthology.org. Checked 22 October 2026.
  3. Jack the Reader – A Machine Reading Framework aclanthology.org. Checked 22 October 2026.
  4. Axial Attention in Multidimensional Transformers arxiv.org. Checked 22 October 2026.
  5. Object-Centric Learning with Slot Attention papers.nips.cc. Checked 22 October 2026.
  6. Object-Centric Learning with Slot Attention research.google. Checked 22 October 2026.
  7. Transformers for Image Recognition at Scale research.google. Checked 22 October 2026.
  8. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale arxiv.org. Checked 22 October 2026.
  9. Simple Open-Vocabulary Object Detection with Vision Transformers research.google. Checked 22 October 2026.
  10. Simple Open-Vocabulary Object Detection with Vision Transformers arxiv.org. Checked 22 October 2026.
  11. Designing In Vivo CAR-T with the Inceptive Platform inceptive.com. Checked 22 October 2026.
  12. From better models to better in vivo CAR-T inceptive.com. Checked 22 October 2026.