Lyft interview notes, page 1 of 10

Large Enterprises

Most of these Lyft write-ups come from Software Engineers (41), then Product Managers (28) and Data Scientists (19). The most reported rounds are the technical phone screen (42), the product sense round (29), the system design round (12) and the behavioral / leadership round (4).

97 interview notes · updated Sep 2026

Lyft·Data Scientist·Technical Phone Screen

Sep 2025

Lyft data scientist interview that was basically a full SQL gauntlet with schema handed to you upfront. Four tasks, progressively harder, and the rolling window piece at the end is where things got uncomfortable.

Given an events table and an orders table, compute for each city and date: unique users with at least one impression, completed orders using the latest status per order, conversion rate, and a 7-day rolling conversion rate per city.
For each supplier and date in the same window, compute demand as unique non-cancelled orders created on that date, then compute fill rate as min(demand, units_available) divided by demand. Handle missing inventory dates by defaulting units_available to zero.
If an order is created on one date but completes on a later date, which date should it contribute to for demand vs. completed orders? Explain your definition and justify it.
Write SQL that handles duplicate rows in the orders table by selecting only the latest status per order using window functions. Then sketch the equivalent pandas code, including the rolling metric and safe division.

“The funnel part was fine but the rolling window is where I got tripped up.”

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Lyft·Software Engineer·Technical Phone Screen

Apr 2026

Lyft SWE phone screen that was basically Meeting Rooms II but with a twist that completely changes the data structure approach. Felt like a reasonable problem until they asked about the assignment history output and I had to rethink everything.

Given a stream of job records in CSV format where each line contains a start time and duration (e.g. '1030 30' for a 30-minute job starting at 10:30), parse the stream and determine the minimum number of workers needed to cover all jobs without overlap. Then print the full assignment log showing which worker handled which job.
When multiple workers are all free at the moment a new job arrives, how do you assign the job, and what data structure supports that efficiently?
What is the time and space complexity of your solution?

“The minimum workers part I got pretty quickly, standard interval scheduling with a min-heap on end times.”

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Lyft·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Did a system design round at Lyft for a software engineer role and it was a strange experience. The interviewer had a stern face the whole time and jumped into tradeoffs before I even finished clarifying requirements, then asked zero follow-up questions about the actual design.

Design a donation system.

“The weird part wasn't the question itself, it was the pacing.”

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Lyft·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Lyft product interview, one question about a trade-off feature design that sounds deceptively simple but ends up pulling in a lot of directions at once.

How would you design, analyze, and roll out an opt-in feature for Lyft riders that offers lower prices in exchange for longer wait times?

“This one has more surface area than it looks.”

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Lyft·Data Scientist·Technical Phone Screen

Apr 2026

Lyft data scientist interview with a heavy algorithmic focus, two connected problems around ride-sharing dispatch that pushed well beyond typical DS territory into competitive programming land. Not what I expected walking in.

Given historical rider demand density and current driver GPS locations, design an algorithm to reposition idle drivers in real time so that expected pickup time across the city is minimized.
Given N ride requests each defined by a start and end timestamp, return the minimum number of drivers needed so that every ride is served with no two overlapping rides assigned to the same driver.

“I went straight to greedy nearest-neighbor and the interviewer let me run with it for a while before nudging me toward thinking about global vs local optima.”

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Lyft·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Lyft PM interview with a product sense question about a pretty specific feature. Not a lot of context given upfront, which made it harder than it sounds.

Define the goals and success metrics for a default pick-up location pin feature.

“I went straight to rider convenience and started rattling off metrics like ETA accuracy and cancellation rate.”

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Lyft·Software Engineer·Technical Phone Screen

Apr 2026

Lyft coding interview, pretty standard algorithmic problem but they had me write my own test cases in the environment which I wasn't expecting.

Given an array of intervals, merge all overlapping ones and return a list of non-overlapping intervals that cover the same range.

“I knew this problem.”

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Lyft·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Lyft ML Engineer screen that leaned heavily on software fundamentals, more than I expected for an ML role. Six questions, all backend/systems flavored, nothing about modeling or data pipelines.

A production system is running very slowly. Walk through how you'd find the bottleneck, measure it, and fix it.
A system was working fine but starts crashing after running for a while. How would you debug and resolve it?
What are generators and decorators in Python?
Why is the 'with open(...)' pattern preferred over manually opening and closing files in Python?
What's the difference between synchronous and asynchronous execution, and when would you use each?
Explain multithreading, and describe the differences between threads and processes. When would you pick one over the other?

“I went straight to profiling and tracing, talked about CPU vs I/O bound issues, mentioned APM tooling.”

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Lyft·Software Engineer·Online Assessment (OA)

Apr 2026

Lyft SWE online assessment, just one problem: the Rotting Oranges grid BFS thing. Pretty standard as far as OAs go.

Given a grid where some oranges are rotten and spread rot to adjacent fresh oranges each minute, find the minimum time for all oranges to rot (or return -1 if impossible).

“Classic multi-source BFS setup.”

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Lyft·Software Engineer·Technical Phone Screen

Apr 2026

Lyft software engineer interview with a classic sliding window problem. Nothing too exotic but the implementation details matter more than you'd expect.

Given two strings s and t, find the smallest substring of s that contains every character in t, including duplicates. Return an empty string if none exists.

“I knew the sliding window pattern going in but still fumbled the frequency map bookkeeping.”

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