Tal Schuster
Senior Staff Research Scientist at Google DeepMind
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolTal Schuster is a Senior Staff Research Scientist at Google DeepMind with nine years of experience building production-ready ML and LLM systems. He leads research and engineering on adaptive compute modeling for large language models—work that has landed in Gemini and other flagship products—while also improving model robustness against adversarial and real-world challenging inputs. His background spans applied deep learning for medical imaging and personalized cancer risk prediction, translating academic research from MIT into clinical-impact algorithms. Prior leadership in military intelligence engineering and full-stack contributions to open-source projects like microsoft/PythonProgrammingPuzzles underline his blend of systems-level pragmatism and research rigor. Tal combines a PhD in Computer Science with hands-on product delivery, making him skilled at moving novel ML methods into scalable, trustworthy deployments.
9 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Massachusetts Institute of TechnologyMaster’s Degree, Computer Science, Master’s Degree, Computer Science at Tel Aviv UniversityBachelor of Science (B.Sc.), Mathematics and Computer Science, Bachelor of Science (B.Sc.), Mathematics and Computer Science at Ben-Gurion University of the Negev
Github Skills (6)
jupyter-notebook10
python10
ai9
ipython8
back-end-development6
front-end-development6
Programming languages (4)
Github contributions (5)
microsoft/PythonProgrammingPuzzles
Jun 2021 - Jul 2021
A Dataset of Python Challenges for AI Research
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:8 commits, 4 PRs, 1 comment in 1 month
Contributions summary:Tal focused on enhancing the demo's functionality and user experience. Their work included refactoring notebook cells, addressing issues with the initial solution, and updating the overall instructions within the demo. Further, they also modified the demonstration's underlying code and related documentation to better represent the system and overall functionality. They appear to have incorporated improvements to both front-end and the back-end study code.
programming-competitionspythondata-sciencedatasetmachine-learning
TalSchuster/BiorxivRetriever
May 2020 - Dec 2020
Contributions:14 commits, 13 pushes, 1 branch in 7 months
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