Maxwell Bileschi
Staff Research Software Engineer at Google
Waltham, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolMaxwell Bileschi is a Staff Research Software Engineer at Google with 15 years of experience building large-scale infrastructure and machine learning systems, currently focused on AlphaFold Gemini and protein function prediction. He blends deep research sensibilities—contributions to Google Research on deep learning for protein sequence analysis—with pragmatic engineering honed at Twitter (Fabric, video, and big-data backends). Maxwell has a strong applied-math foundation (BA/MA in Mathematics) and a CS degree, which he leverages to design robust model evaluation, sequence processing pipelines, and scalable inference systems. He’s led cross-disciplinary teams, taught university-level mathematics, and formerly sold a game he developed in high school, showing a long-standing habit of shipping working software. Notably, his open-source work supports published research tying convolutional/residual architectures to real protein annotation outcomes.
15 years of coding experience
6 years of employment as a software developer
Webster ThomasBS, Computer Science, BS, Computer Science at SUNY at BuffaloBA/MA, Mathematics, BA/MA, Mathematics at State University of New York at Buffalo
Stackoverflow
2,172reputation
1.1mreached
19answers
6questions
Github Skills (16)
machine-learning10
inference10
deep-learning10
tensorflow10
python10
ai10
neural-network9
convolutional-neural-networks9
pandas6
scipy6
scatter-plot6
urllib6
knn6
numpy6
matplotlib6
Programming languages (9)
Github contributions (5)
google-research/google-research
Jun 2021 - Dec 2022
Google Research
Role in this project:
ML Engineer
ML EngineerContributions:16 commits in 1 year 6 months
Contributions summary:Maxwell contributed code to support publications related to using deep learning for protein annotation. They developed model construction and evaluation metrics, specifically focusing on protein sequence analysis. The code includes modules for sequence processing, representation, and prediction, which involve convolutional layers and residual blocks. Their work also encompassed the integration of saved models for inference.
googlemachine-learningai
mlbileschi/Perl-Quiz
Mar 2011 - Jan 2012
Contributions:51 commits in 10 months
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