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Sherry Moore

Also known as Sherry Q Moore

Sherry Moore is a senior software engineer and researcher at Google DeepMind whose work spans machine-learning systems and robotics. She coauthored TensorFlow at Google Brain and studied evolutionary methods for image-classifier design. Her robotics research included a high-speed table-tennis learning system, the Open X-Embodiment work and a robot that reached amateur human-level play. As of October 2026, she leads initiatives at the intersection of AI and productivity tools.

Works
7
Citations
21,754
h-index
7

Publication numbers from OpenAlex, as of 15 October 2026.

Timeline

  1. 2015

    TensorFlow as open source

    Moore coauthors TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, a paper that describes an interface for machine-learning algorithms. Google releases the TensorFlow API and reference implementation as open-source software in November 2015.

  2. 2016

    Presenting TensorFlow at OSDI

    At Google Brain, she presents TensorFlow: A System for Large-Scale Machine Learning at OSDI 2016.

  3. 2017

    Evolving image classifiers

    In 2017, she coauthors Large-Scale Evolution of Image Classifiers with Esteban Real and colleagues. Their evolutionary algorithms discover neural-network architectures for CIFAR-10 and CIFAR-100.

  4. 2023

    Learning robotic table tennis

    In 2023, she coauthors a case study of a robotic learning system able to rally with a human and return balls to precise targets. The team combines perception, low-latency control, simulation and policy training for transfer to a physical robot.

  5. 2023

    Open X-Embodiment and RT-X

    Also in 2023, she coauthors Google DeepMind's work on Open X-Embodiment and RT-X. The project pools data from 22 robot types with partners from 33 academic labs; RT-1-X transfers skills across robot embodiments.

  6. 2024

    Amateur-level robot table tennis

    In August 2024, she coauthors the study presenting the first learned robot agent to reach amateur human-level performance in competitive table tennis. Across 29 matches against unseen players, the robot wins all matches against beginners and 55% against intermediate players.

  7. As of October 2026

    Senior engineer at Google DeepMind

    As of October 2026, she is a senior software engineer and researcher at Google DeepMind, where she leads initiatives in artificial intelligence and productivity tools. Before Google, she spends 14 years as a systems and kernel engineer at Sun Microsystems; her 15-year Google career includes Google Ads, Google Fiber, TensorFlow and robotics.

Sources and credits

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  1. Sherry Moore research.google. Checked 15 October 2026.
  2. Sherry Moore | USENIX usenix.org. Checked 15 October 2026.
  3. Large-Scale Evolution of Image Classifiers proceedings.mlr.press. Checked 15 October 2026.
  4. Robotic Table Tennis: A Case Study into a High Speed Learning System roboticsproceedings.org. Checked 15 October 2026.
  5. Scaling up learning across many different robot types deepmind.google. Checked 15 October 2026.
  6. Achieving Human Level Competitive Robot Table Tennis deepmind.google. Checked 15 October 2026.
  7. Speakers - Silicon Valley WiE 2026.siliconvalleywie.org. Checked 15 October 2026.