Julian Eisenschlos
Senior Staff Research Scientist at Google DeepMind
Switzerland
Summary
🤩
Rockstar🎓
Top SchoolJulian Eisenschlos is a Senior Staff Research Scientist at DeepMind and a PhD candidate in Computer Science with a decade of experience building production-ready ML systems and research prototypes across Google, Meta, ASAPP and startups. Trained as a mathematician, he blends rigorous theory—game theory, optimization and algorithms—with hands-on engineering in Python, C/C++ and large-scale data stacks (Hive, Presto, BigQuery). He co-founded BotMaker to scale multilingual conversational AI, contributed practical tooling to the high-profile google-research/tapas repo to make table-text models runnable in Colab, and has a track record of shipping end-to-end solutions from infra to model. Known for self-taught fluency across paradigms and a product-minded view of research, he focuses on turning state-of-the-art techniques into usable, impactful products. Based in Switzerland, he remains active in teaching and community-facing roles, combining academic depth with startup and large-company execution.
10 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Universidad Nacional de CórdobaMaster's degree, Math Science - Licenciatura en Matemática Pura, Master's degree, Math Science - Licenciatura en Matemática Pura at Universidad de Buenos AiresSummer School, Finite Group Representations, Summer School, Finite Group Representations at IMPA
English, Portuguese, Spanish
Stackoverflow
31reputation
571reached
3answers
0questions
Github Skills (8)
tensorflow10
python10
question-answering10
google-colaboratory9
google-colab9
stemming6
poker6
numpy6
Programming languages (8)
Github contributions (5)
google-research/tapas
May 2020 - May 2022
End-to-end neural table-text understanding models.
Role in this project:
ML Engineer
ML EngineerContributions:5 reviews, 23 commits, 3 PRs in 2 years
Contributions summary:Julian primarily contributed to the development and maintenance of the Tapas model, focusing on making the model runnable in a Colab environment. They added a Colab notebook to run the Tapas model and make predictions, including the necessary code for model loading, data conversion, and prediction execution. The user also updated the verbosity level and bumped the TensorFlow version. Further contributions included adding support for MATE and HybridQA training.
understandingend-to-enddeep-learningmachine-learningnlp-machine-learning
eisenjulian/fb-page-chat-download
Jun 2016 - Aug 2019
Python script to download messages from a Facebook page to a CSV file
Contributions:12 commits, 3 PRs, 11 pushes in 3 years 3 months
csvpythonpython-scriptfacebookfacebook-page
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