Elizabeth Santorella - Research Engineer at Meta

Elizabeth Santorella

Research Engineer at Meta

Medford, Massachusetts, United States

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Summary

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Elizabeth Santorella is a Research Engineer at Meta with a decade of experience blending economics, statistics, and engineering to build practical ML and experimentation infrastructure. Trained as an economist (MA, PhD) with an SB in Physics and Economics from MIT, she moved into data science and scientific software, owning end-to-end pricing and causal-inference systems that drove billions in revenue. At Meta she focuses on adaptive experimentation and has contributed to high-profile open-source projects like Facebook's Ax and PyTorch/BoTorch, improving model interfaces, scheduler callbacks, and CI-docstring quality for Bayesian optimization tools. Her strengths are statistical computing, causal inference, and experiment design, coupled with hands-on backend and DevOps work that keeps research code production-ready. Comfortable across Python, C/C++, Spark, and Docker, she also brings mentorship and hiring experience from small-team environments. A detail many miss: she combines rigorous academic research with practical software fixes—like Pareto-frontier and type-checking improvements—that materially improve optimization tooling used by practitioners.

10 years of coding experience

7 years of employment as a software developer

SB, Physics, Economics, SB, Physics, Economics at Massachusetts Institute of Technology

Master's (MA), Economics, Master's (MA), Economics at Harvard University

English, Spanish

Stackoverflow

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Github Skills (16)

model-driven-development10

model-building10

pytorch10

machine-learning10

bayesian10

flake10

python10

optimisation10

modeling10

documentation10

optimization10

model-driven10

testing10

continuous-integration9

api8

Programming languages (5)

C++GoHTMLJupyter NotebookPython

Github contributions (5)

facebook/Ax

Jul 2022 - Jan 2023

Adaptive Experimentation Platform

Role in this project:

Backend Developer

Contributions:23 reviews, 15 commits, 242 PRs in 5 months

Contributions summary:Elizabeth's contributions primarily involve modifying the `ax` library, focusing on the `models` and `modelbridge` submodules. They removed data requirements for TorchModels before candidate generation and raised a `DataRequiredError` in the `BotorchModel.fit` function. The user also added callback functionality to the Ax Scheduler and included an example of how this can be used for in-place figure updates during the optimization process. Additionally, the user addressed issues with the Pareto frontier computation, corrected type signatures and other fixes related to outcome constraints for MOO and other type-checking improvements.

experimentationadaptivesimulationadaptive-experimentation-platformexperimentation-platform

pytorch/botorch

Jul 2022 - Jan 2023

Bayesian optimization in PyTorch

Role in this project:

Backend Developer & DevOps Engineer

Contributions:3 releases, 263 reviews, 36 commits in 5 months

Contributions summary:Elizabeth contributed to the project by fixing docstrings and adding checks for them in the continuous integration workflow using flake8-docstrings. They updated the project documentation by expanding the docstrings and creating new documentation for models. The user also fixed failing tests related to long docstring lines, and optimized the optimization and error handling of functions called during model training and acquisition function optimization.

pytorchoptimizationmultiobjective-optimizationmachine-learningbayesian-optimization

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