About

Leah Dineen is a highly experienced Senior Software Engineer with a strong background in risk engineering, machine learning, and backend development. Currently working at Plaid as a Senior Software Engineer, Leah has modernized the TLS fingerprinting service, improving observability and error handling, and bringing the service onto Plaid's latest CI/CD tools. She has also partnered with ML Infra as an early adopter of Tecton as a feature store at Plaid, implementing Risk's first features in Tecton and making them available for use in the rule engine. Prior to joining Plaid, Leah worked at Lyft as a Senior Software Engineer in Fraud & Integrity. She built systems to fight fraud and abuse at scale, working across teams and functions to support understanding, adoption, and define the future of these systems. As the expert on several key systems, Leah led backend SWE and ML model development for the credit card scanner, building a card scan challenge that increased bookings by 80% with no significant increase in chargebacks. She also designed and implemented a new service to manage fraud challenges, building a highly scalable system that could surface a challenge on any server response, without needing client app changes. Leah's experience also includes working as a Software Engineering Intern at Lyft, where she automated re-training of machine learning models which classify fraudulent users and changed how Lyft stores and applies fraud models to use scikit-learn instead of in-house defined python classes. Leah holds a Bachelor of Computer Science from the University of Waterloo, where she studied computer science and gained a strong foundation in software engineering. She has a wide range of technical skills, including expertise in software engineering, machine learning, CI/CD, infra, and backend development. With 5.53 years of relevant experience, Leah is a highly skilled and experienced Senior Software Engineer with a proven track record of success in building scalable, efficient, and effective systems.