Building scalable AI and MLOps platforms at Google. Former guest researcher at the Flatiron Institute Center for Computational Astrophysics. M.S. from NYU Center for Data Science.
About
I'm a Software Engineer III at Google Cloud AI, where I contribute to the ADK framework—a suite of tools for deploying AI agents. Previously at Core Machine Learning, I built scalable MLOps infrastructure with a focus on developer velocity, and led the development of a Gemini-powered agent for diagnosing pipeline failures.
Before Google, I was a Guest Researcher at the Flatiron Institute of the Simons Foundation, applying AI to longstanding problems in astronomy. This work was published at the NeurIPS 2022 Machine Learning and the Physical Sciences Workshop.
I hold an M.S. in Data Science from NYU (where I studied Deep Learning under Yann LeCun) and a B.A. in Business from Rhodes College, where I received the John M. Planchon Award for Excellence in Business, given to the most outstanding graduating business major as chosen by the business faculty.
Outside of engineering, I'm passionate about music and creative expression.
Contributing to the internal ADK framework, a suite of tools for deploying AI agents.
Led a major refactor of credential management to unify authentication across both local and distributed cloud environments.
Reduced agent build latency by ~37% and improved developer efficiency by integrating a remote build execution framework.
Core Machine Learning (Cloud AI)
Worked on an MLOps platform enabling users to author and deploy ML workflows.
Designed and launched a scalable alert infrastructure in C++ utilizing distributed database changelogs to notify users of real-time pipeline state changes, including engineering APIs for user-defined metrics.
Spearheaded an initiative to increase build velocity, slashing P80 pipeline build times from ~2 minutes to seconds by integrating a remote build execution framework (earned VP-level commendation).
Led a PhD intern in architecting a Gemini-powered agent within the ADK framework that diagnoses pipeline failures. Oversaw the project end-to-end, from prompt design to tool integration to evaluation.
Software Engineer II
Google
August 2022 – May 2024
Analyzed and optimized a migration tool for Tensorflow Extended (TFX) pipelines, identifying 50% automated coverage and resolving critical feature gaps to unblock the platform's General Availability launch.
Designed a component-generated alerting framework for users to configure alerts based on custom execution outcomes and specific logic needs.
Executed a debuggability refactor to surface root-cause errors and logs directly in the UI.
Designed and launched a new CLI tool to streamline dev workflows, adopted by hundreds of users.
Guest Researcher
Flatiron Institute Center for Computational Astrophysics
August 2021 – June 2022
Worked under Dr. Shirley Ho applying AI to longstanding problems in astronomy, like noisy satellite imaging.
First authored a paper describing the use of radiance fields to combine and extract meaningful information from star images.
Data Science Intern
Q2 Software
Summer 2021
Developed a client-level recommender system using PySpark to drive cross-selling strategies and increase profitability.
Implemented comprehensive integration and unit testing suites for pre-production models.
Publications
Astronomical Image Coaddition with Bundle-Adjusting Radiance Fields
First Author • NeurIPS 2022 Machine Learning and the Physical Sciences Workshop
As a guest researcher at the Flatiron Institute of the Simons Foundation, applied bundle-adjusting radiance fields to combine, de-noise, and remove obstructions from cosmological observations across varying resolutions and noise levels.
Deep Learning (taught by Yann LeCun), Natural Language Processing, Machine Learning & Computational Statistics, Responsible Data Science, Computational Cognitive Modeling, Big Data, Probability & Statistics
John M. Planchon Award for Excellence in Business (awarded to the most outstanding graduating business major)
Skills
Languages
PythonC++SQLLanguage Agnostic
Native English, intermediate Spanish
Core Competencies
AI Assisted CodingLLM IntegrationPrompt EngineeringAgentic WorkflowsSystem DesignMLOpsTechnical StrategyAPI ArchitectureDistributed Systems
Music
The Loud Women Project — A Rhodes Fellowship documentary
Music has always been a vital part of who I am. Creating and performing allows me to explore a different kind of expression—one that complements the analytical nature of engineering with raw creativity and emotion.
The Loud Women Project, created during my Rhodes Fellowship, documents the stories of women in the music industry, exploring themes of creativity, resilience, and artistic expression.
My song "Anyways" was featured in Amazon's original series Motorheads.
