Engineering articles from Lyft
AI summaries and key learnings from Lyft engineering teams.
From Python3.8 to Python3.10: Our Journey Through a Memory Leak
This article chronicles the experience of upgrading Python services from version 3.8 to 3.10 at Lyft, highlighting a significant memory leak issue encountered during the transition. The author...
FacetController: How we made infrastructure changes at Lyft simple
The article discusses Lyft's implementation of FacetController, a tool designed to streamline the management of Kubernetes deployments through the use of Custom Resource Definitions (CRDs). By...
From manual fixes to automatic upgrades — building the Codemod Platform at Lyft
The article outlines the development of the Codemod Platform at Lyft, aimed at automating the process of upgrading libraries and managing code transformations across numerous frontend microservices....
Real-Time Spatial Temporal Forecasting @ Lyft
The article discusses the implementation of real-time spatial temporal forecasting models at Lyft, focusing on their application for predicting market conditions critical for operational efficiency....
Beyond Query Optimization: Aurora Postgres Connection Pooling with SQLAlchemy & RDSProxy
The article explores the importance of efficient database connection management, particularly in the context of PostgreSQL and SQLAlchemy. It emphasizes the benefits of connection pooling to reduce...
How science inspires our ETA models
The article explores the relationship between chaotic traffic patterns and the development of accurate travel time predictions. It highlights the importance of understanding micro and macro patterns...
Solving Dispatch in a Ridesharing Problem Space
The article delves into the complexities of dispatch systems in ridesharing platforms, particularly focusing on the mathematical and algorithmic aspects of matching drivers to riders. It explains how...
Intern Experience at Lyft
The article outlines the experiences of two data scientists at Lyft, detailing their internships and subsequent full-time roles. It emphasizes the application of data science in evaluating electric...
Migrating Lyft’s Android Codebase to Kotlin
The article outlines Lyft's journey in migrating its Android codebase from Java to Kotlin, a process initiated in 2018 and completed in 2025. Key motivations for this transition included Kotlin's...
My Starter Project on the Lyft Rider Data Science Team
The article outlines a data science project undertaken by a new hire at Lyft, focusing on the Rider Experience Score (RES) tool to analyze the long-term effects of rider experiences on retention. It...
LyftLearn Evolution: Rethinking ML Platform Architecture
The article outlines Lyft's journey in evolving its machine learning platform, LyftLearn, to address the complexities and bottlenecks associated with its original Kubernetes-based architecture. It...