Leah Mcguire
Machine Learning Engineer at Faros AI
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolLeah Mcguire is a Machine Learning Engineer with 11 years of experience building production ML systems and data infrastructure across startups and large tech companies, currently at Faros AI in the Greater Seattle area. Her background spans research-grade neuroscience (Ph.D.) to applied ML and data engineering at LinkedIn, Salesforce (Einstein), and Benchling, demonstrating a rare fluency in both statistical modeling and scalable backend systems. She has contributed to well-known open-source projects—improving Avro support in Apache Spark and enhancing model selection metadata in Salesforce’s TransmogrifAI—highlighting her focus on robust serialization and reproducible AutoML workflows. Leah combines rigorous experimental skills from academia with practical engineering that ships: she’s redesigned data pipelines, APIs, and core libraries to improve reliability and observability at scale. Colleagues can expect a pragmatic problem-solver who bridges research, tooling, and production deployments.
11 years of coding experience
21 years of employment as a software developer
Ph.D, Neuroscience, Ph.D, Neuroscience at UC San FranciscoBA, Biology, BA, Biology at UC Santa Barbara
Github Skills (21)
apache-spark10
avro10
multiple-selection10
feature-selection10
dataframes10
machine-learning10
data-serialization10
metadata10
schema-design10
dataframe10
scala10
serialization10
schema-org10
automl10
variable-selection10
Programming languages (8)
Github contributions (5)
salesforce/TransmogrifAI
Aug 2018 - Oct 2020
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:18 reviews, 40 commits, 79 PRs in 2 years 2 months
Contributions summary:Leah primarily contributed to the implementation and modification of case classes and interfaces related to model selection metadata within the TransmogrifAI library. Their work focused on creating and updating Scala case classes for storing model selector summaries and evaluations, enhancing the system's ability to manage and track model performance. The commits reflect efforts to improve the handling of model metadata, which is crucial for AutoML systems. The user implemented several core classes and functionalities within the library.
pythonsalesforcetransformationstransmogrifyestimators
databricks/spark-avro
Apr 2015 - Apr 2015
Avro Data Source for Apache Spark
Role in this project:Back-end Developer
Back-end DeveloperContributions:5 commits, 1 PR, 1 comment in 6 days
Contributions summary:Leah primarily focused on enhancing the Avro data source functionality for Apache Spark. They implemented features allowing users to specify record names and namespaces during Avro saving, which involved modifying the `AvroSaver`, `SchemaConverters`, and `AvroRelation` classes. Their contributions also included refactoring the code for improved style and updating the API for parameter passing. These changes centered on improving data serialization and deserialization processes, central to the library's purpose.
avroavro-datadata-sourceapachebig-data
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