Anthony Polloreno
Machine Learning Infrastructure Engineer at Apple
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolAnthony Polloreno is a machine learning infrastructure engineer in San Francisco with a decade of experience blending research-grade mathematics, large-scale model training, and quantum-computation expertise. He builds scalable training systems and orchestration for LLMs, having led hyperparameter and scaling experiments at Essential AI and now focusing on infrastructure at Apple. His background spans hands-on quantum control and characterization work (including major contributions to Rigetti’s pyQuil and device calibration suites) through to theoretical research on reservoir computing during a physics PhD. Anthony’s strength is translating rigorous, often physics-driven algorithms into production-quality software that improves experimental throughput and hardware efficiency. He brings a rare combo of low-level systems chops, ML throughput engineering, and proven open-source impact on widely used quantum tooling.
10 years of coding experience
5 years of employment as a software developer
Bachelor of Arts (B.A.) Computer Science Mathematics Physics, Bachelor of Arts (B.A.) Computer Science Mathematics Physics at University of California, BerkeleyDoctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at University of Colorado Boulder
English, Japanese
Stackoverflow
101reputation
0reached
0answers
0questions
Github Skills (9)
unit-testing10
quantum-computing10
quil10
python10
pytest9
develop9
data-structure7
data-structures7
algorithms7
Programming languages (8)
Github contributions (5)
rigetti/pyquil
Jan 2017 - Jun 2018
A Python library for quantum programming using Quil.
Role in this project:
Backend Developer
Backend DeveloperContributions:27 commits, 22 PRs, 20 pushes in 1 year 4 months
Contributions summary:Anthony primarily focused on enhancing the pyQuil library by addressing resource manager bugs and expanding functionality. Their contributions included bug fixes in the resource manager, implementing new features like Pauli term and sum power, and improving code coverage through the addition of tests. They also addressed issues in the Quil program, including support for qubit handling and extraction.
pythonforestquilquantum-computingquantum-programming
ampolloreno/ampolloreno.github.io
Dec 2017 - May 2026
Contributions:3 PRs, 275 pushes, 1 branch in 8 years 5 months
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