Lyft Engineering
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Stories from Lyft Engineering. Read about the data science and engineering, and more behind the rideshare app. Lyft is your friend with a car, whenever you need one.
Lyft Engineering
2M ago
At Lyft Media, we’re obsessed with building flexible and highly reliable native ad products. Since our technical stack encompasses mobile clients on both iOS and Android, as well as multiple backend services, it is crucial to ensure robust and efficient communication between all involved entities. For this task we are leveraging Protocol Buffers, and we would like to share the best practices that are helping us achieve this goal. This article focuses on our experience addressing the challenges that come with collaborating on shared protocols in teams where people with different levels of fami ..read more
Lyft Engineering
3M ago
Building Lyft’s Next Emblem — Glow
By: Avneet Oberoi, Michael Vernier, Phoenix Li, Masroor Ahmed
Introduction
Long time riders might remember the original fuzzy, pink Carstache emblem that made Lyft universally recognizable. Over the years, the emblem dropped the fuzz for pink lights in the Glowstache and later evolved with more colors as the beloved Amp, which has been in active use for over seven years! Recently, Lyft has introduced its brighter, bolder next generation emblem — Glow. Glow provides a daytime visible, auto-dimmable display showing rider customizable colors and new animati ..read more
Lyft Engineering
4M ago
Written by Nada Sarsour, Maria Rice, and Kelly Haberl
Interested in applying to Lyft Data Science or currently in the interview process? This article helps answer questions commonly asked by Data Science candidates looking to learn more about the Lyft application process, our Data Science teams, and overall life at Lyft!
The Application & Interview Process
Which Data Science role should you apply for?
Lyft Data Science is split between Decisions and Algorithms Scientists, which can be defined by the typical output of their work (although there is overlap between the 2 roles ..read more
Lyft Engineering
5M ago
The ETA Conundrum: Speed vs Accuracy
Imagine this: You’ve got a crucial early morning flight to catch — you open the Lyft app, and see an estimated pickup time on the screen. But here’s the million-dollar question: Will the driver you are paired with, arrive at the estimated time? One of Lyft’s most simple, yet profound goals is to ensure we provide riders with the most accurate ETAs we can.
Before you even hit ‘request’ and summon a ride, there are complex algorithms that sift through historical and real-time data, leveraging machine learning alongside traffic and weather insights t ..read more
Lyft Engineering
7M ago
Written by Shumpei Goke and Jinshu Niu
Why Technical Learning?
At Lyft, data scientists tackle challenging technical problems every day. To support and empower our data scientists, Lyft’s Technical Learning Council (TLC) provides diverse and high-quality continuous learning opportunities to hone their technical skills. TLC’s mission is “to equip Data Science team members with the technical knowledge and skills that are applicable to their work and helpful to their career advancement.” Investing in technical learning not only aids data scientists in solving complex problems but also contri ..read more
Lyft Engineering
8M ago
Summary
At Lyft we have built a platform for developing, training and serving Reinforcement Learning models for typical internet industry applications with a focus on Contextual Bandits. Those models have been critical for decision making problems that other techniques such as supervised learning or optimization models struggled with. In this article we describe how we extended our existing machine learning ecosystem to support Reinforcement Learning models, develop models using Off-Policy Evaluation, and the lessons learned along the way.
Introduction
Reinforcement Learning (RL) has show ..read more
Lyft Engineering
8M ago
Photo by Frank Olsen UNDER CC BY-SA 3.0 DEEDIntroduction
Our payment platform team had the unique challenge to upgrade our Aurora Postgres DB from v10 to v13. This DB was responsible for storing transactions within Lyft and contains ~400 tables (with partitions) and ~30TB of data. Upgrading the database in-place would have resulted in ~30 mins of downtime. Such significant downtime is untenable — it would cause cascading failures across multiple downstream services, requiring a large amount of engineering effort to remediate. Through native replication between the two Postgres major versi ..read more
Lyft Engineering
8M ago
PUP in action | Photo by philhearing under CC BY 2.0 DEED
In this post, we’ll cover how Lyft upgrades Python at scale — 1500+ repos spanning 150+ teams — and the latest iteration of the tools and strategy we’ve built to optimize both the overall time to upgrade and the work required from our engineers. We’ve successfully used (and evolved) this playbook over multiple upgrades, from Python 2 to Python 3.10 and hope you find it useful!
Welcome to BLT! Here’s your first assignment.
The Backend Language Tooling (BLT) team at Lyft is responsible for the Python and Go experience for our en ..read more
Lyft Engineering
1y ago
Written by Ritesh Varyani and Jeana Choi at Lyft.
Introduction
At Lyft, we have used systems like Apache ClickHouse and Apache Druid for near real-time and sub-second analytics. Sub-second query systems allow for near real-time data explorations and low latency, high throughput queries, which are particularly well-suited for handling time-series data. For our customers, this means faster analytics on near real-time data and decision making. This is crucial for use cases like market signaling and forecasting which benefit from, and depend upon, the most up-to-date information. Overall, the ..read more
Lyft Engineering
1y ago
High-quality data is necessary for the success of every data-driven company. It enables everything from reliable business logic to insightful decision-making and robust machine learning modeling. It is now the norm for tech companies to have a well-developed data platform. This makes it easy for engineers to generate, transform, store, and analyze data at the petabyte scale. As such, we have reached a point where the quantity of data is no longer a boundary. Yet this has come at the cost of quality.
In this post we will define data quality at a high-level and explore our motivation to ach ..read more