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Lyft interview notes, page 6 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·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Lyft focused on a product improvement case around the default pick-up location feature. Pretty classic product sense format, one question, no fluff.
  • You're a PM at Lyft owning the default pick-up location feature. How would you go about improving it?

“I spent the first minute or so just trying to figure out what 'default pick-up location' even means in practice, like is it GPS-based, is it learned from history, does it factor in building entrances.”

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Lyft·AI Engineer·Technical Phone Screen

Jun 2026
Lyft AI Engineer technical screen, pretty deep on ML fundamentals. One question but it had a lot of surface area and they clearly wanted more than a textbook answer.
  • Walk me through the difference between a general-purpose transformer model (like GPT or BERT) and an embedding model (like sentence-BERT or OpenAI's text-embedding series). Cover what each is trained to do, the training objectives involved, what the outputs actually represent, typical use cases for each, and when you'd pick one over the other in a real system like RAG.

“This looked like a single question but it was really five questions jammed together.”

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Lyft·Software Engineer·Technical Phone Screen

Jun 2026
Lyft software engineer screen, one coding problem the whole time. The twist was no test cases handed to you, you had to come up with your own, which honestly changed the feel of the whole thing.
  • Given two strings, find the shortest contiguous substring of the source string that contains all characters of the target string, including duplicates. Return an empty string if none exists. You must also design your own test cases.

“The sliding window part I was fine with, two pointers, frequency maps, shrink from the left when valid.”

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Lyft·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Lyft PM interview with a product strategy question framed around Airbnb. One question, fairly open-ended, and the kind where you can go in a lot of directions which is both the appeal and the problem.
  • You're a PM at Airbnb. How would you decide whether to require all hosts to use professional photos for their listings?

“The question sounds like a policy call but it's really a metrics and tradeoffs problem underneath.”

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Lyft·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Lyft centered on a pretty open-ended product strategy case about vertical take-off vehicles. One meaty question, no fluff.
  • Lyft is considering investing in vertical take-off and landing vehicles that seat up to four passengers. How would you approach entering this market, who would you target first, and what product features would matter most?

“This one is deceptively wide open and I kind of froze trying to figure out where to even start.”

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Lyft·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Lyft for a software engineer role. The prompt was a charity donation platform with a lot of moving parts, payment reliability being the main focus. Pretty dense for a single session.
  • Design a charity donation website that uses an external payment SDK. How do you handle payment failures, partial failures, and ensure no donations are lost or recorded with wrong amounts?
  • How would you design the system to handle sudden traffic spikes during high-profile donation campaigns?
  • Design the APIs for donating, viewing donation history, and generating receipts.
  • What does your data model look like for donations, donors, and campaigns?
  • How would you approach observability for this system, and what signals would you monitor for fraud?

“This was the core of the whole thing.”

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Lyft·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM case question at Lyft, pretty classic product sense format. Just one question but they pushed hard on the prioritization piece, which is where things got interesting.
  • You're the PM for an amusement park where popular rides have an average wait time of one hour. Brainstorm ways to address this and explain how you'd prioritize them.

“I jumped straight into solutions and had to backtrack when they asked me to slow down and define the problem better first.”

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Lyft·Data Scientist·Technical Phone Screen

Jun 2026
Did a machine learning system design round for a DS role at Lyft. One question, focused on churn prediction for riders. Pretty standard for this type of role but there's enough depth in the schema discussion to keep you busy.
  • Given a provided data schema, how would you design a model to predict whether a passenger will churn in the next few weeks?

“The schema part is what trips people up.”

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Lyft·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Lyft PM interview with a classic metrics diagnostic question. Pretty standard case format but the specificity of the number (45%) made me second-guess myself more than I should have.
  • Ride orders on Lyft dropped by 45%. How would you diagnose what's going on?

“I started with clarifying questions about the timeframe and whether it was across all markets or localized, which felt right.”

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Lyft·Data Scientist·Technical Phone Screen

Jun 2026
Lyft data scientist interview focused entirely on a two-part metric investigation case: a 7% drop in monthly active riders and a 20% spike in average wait time, both in the same month. The case was more layered than I expected and the follow-ups pushed hard on causality and experiment design.
  • Monthly Active Riders dropped 7% month over month. Walk me through how you would investigate this from start to finish.
  • Average ride wait time went up 20% in the same month. How do you investigate that?
  • Before assuming either metric movement is real, what would you check first?
  • If the entire MAR drop turns out to be concentrated in one large metro, how does that change your conclusion and your recommendation?
  • Could the wait time increase be causing part of the MAR decline? How would you test for that causal link and try to quantify it?
  • A pricing change shipped mid-month in only half the metros. How would you use that rollout to estimate its causal effect on both metrics?
  • After shipping a fix, how do you confirm the metric actually recovered because of your fix and not just natural reversion to the mean?

“My instinct was to jump straight into segmentation by metro and cohort, but I caught myself and backed up to data validation first.”

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