Intuit Interview Questions — Page 3

Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit SWE interview that went deep on caching. The LRU part felt manageable but the LFU follow-up is where things got real, and I don't think I handled the tie-breaking explanation as cleanly as I should have.

Design an in-memory LRU cache with capacity N that supports O(1) get and put operations. Walk through your data structure choices and analyze time and space complexity.
Now modify your design to support an LFU cache with O(1) or amortized O(1) operations. How do you track and update frequencies on get and put, and how do you break ties using recency?

“Went with a hashmap plus a doubly linked list, which is the standard answer, and I knew it going in.”

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Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit SWE interview with a low-level systems question that felt a bit retro but made sense once I thought about it.

Design a queue data structure using static memory allocation.

“My first instinct was to just describe a linked list and I had to stop myself.”

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Intuit·Data Scientist·Technical Phone Screen

May 2026

Interviewed for a Data Scientist role at Intuit and got a coding question that felt more like a software engineering screen than anything data-related. Just the one problem but they pushed pretty hard on the follow-ups.

Implement a function that returns the nth Fibonacci number. Be prepared to discuss time and space complexity and how you'd handle very large inputs.

“Started with the naive recursive solution because my brain just went there first, and they immediately asked about stack overflow.”

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Intuit·Product Manager·Onsite - Product Sense / Strategy

May 2026

A product sense question from an Intuit interview, focused on how you'd approach building a restaurant reservation feature for Yelp. Pretty open-ended, more strategy than execution.

Yelp wants to break into the restaurant reservation space but hasn't gained traction. If you were the PM, how would you define the next steps and design the product?

“My first instinct was to jump straight into features, which was a mistake.”

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Intuit·Software Engineer·Onsite - Product Sense / Strategy

May 2026

Product-flavored system design question at Intuit for a software engineer role, focused on when to reach for AI versus just writing normal code. One question, but it had a lot of surface area.

How would you decide whether a new feature should use AI or a traditional rule-based solution? Walk through what makes a problem a good fit for AI, what types of problems should avoid it, what an AI-native product actually means versus just bolting on an AI feature, and the practical trade-offs around accuracy, latency, cost, explainability, and user trust.

“This question has a lot of moving parts and I think I underestimated it at first.”

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Intuit·Product Manager·Onsite - Product Sense / Strategy

May 2026

PM interview at Intuit, one question about measuring metrics for TurboTax. Pretty product-analytics-heavy, not much else to go on from what I remember.

How would you define and measure the key metrics for TurboTax?

“This tripped me up more than it should have.”

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Intuit·Data Scientist·Technical Phone Screen

May 2026

Intuit DS interview focused entirely on applied analytics, three connected problems around a TurboTax metric drop, a promo evaluation, and an A/B test design. Heavy on causal reasoning and experiment design, lighter on coding. Felt more like a product analytics case study than a traditional DS screen.

A TurboTax dashboard shows start-to-file conversion dropped from roughly 18% to 12% overnight with no obvious change in traffic or spend. How do you investigate this?
After localizing the KPI drop, what are the most likely root causes and what specific data would you pull to confirm each one?
You have user-level data from a past promotion offering a discount to some users. How do you determine whether the promotion actually drove incremental filing behavior versus just rewarding people who would have filed anyway?
What primary metrics, diagnostic metrics, and guardrails would you use to evaluate whether the promotion was successful?
Design an A/B test for a new onboarding flow meant to increase filing completion. Walk through randomization unit, primary metric, power calculations, and your analysis plan.
If the primary metric improves in your A/B test but a guardrail metric gets worse, what do you recommend?

“My first instinct was to jump straight to user behavior explanations, which was wrong.”

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Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit software engineer interview, technical phone screen focused on tree traversal with a custom query system. Pretty interesting problem actually, not your typical LeetCode grind.

You're given an array encoding a rooted tree where each element stores the parent of that node. You receive multiple queries, each starting at some node and executing a sequence of moves (go to parent, or go to k-th child by sorted order). Return the final node after all moves. How do you preprocess the tree and handle each query efficiently?

“The encoding tripped me up for a second because it's 1-indexed parents stored at 0-indexed positions, and I kept confusing myself on the offset.”

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Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit technical screen for a software engineer role, one coding problem the whole time. The problem was graph-based and felt pretty applied, like something you'd actually build for a product feature rather than a pure leetcode grind.

Given a set of business-to-business relationships (undirected edges) and a source and target business, find the shortest path between them and return both the path and the degree of separation, where degree is defined as the number of intermediate nodes on that path.

“BFS was the right move and I knew that pretty quickly.”

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Intuit·Software Engineer·Onsite - System Design / Architecture

May 2026

System design interview at Intuit for a software engineering role, focused entirely on building an ML-powered recommendation system for their suite of financial products. Heavy on ML system design, privacy constraints, and business metrics thinking. The depth expected was pretty serious.

Design an ML-powered recommendation system for Intuit that suggests premium products, upgrades, and educational content to users across multiple surfaces like dashboards, email, and an AI assistant, with the goal of increasing helpful adoption rather than just clicks.
What counts as a successful recommendation outcome: first conversion, activated conversion where the user actually uses the product, or retained revenue net of refunds and churn?
Purchase and renewal labels are delayed by days or months and are rare. How do you train and evaluate a ranker before those labels mature, and how do you avoid optimizing a proxy like clicks that drifts from the true objective?
The system selects who sees an offer and then learns from the outcomes of the people it selected. How do you prevent the model from collapsing onto a narrow user segment, and where does an explore/exploit approach fit given the trust constraints around financial recommendations?
Conversions are rising but so are refund and cancellation rates. How would you detect this in monitoring and what would you change in the ranking objective or guardrails?
A regulator asks why a specific user received a specific recommendation. What do you need to log and surface to answer that, and how does this constrain your use of black-box models and generative copy?
Where does generative AI fit in this recommendation system, and where should it explicitly not be used?
What data signals are legally and contractually usable for personalization, and how do you handle users who have opted out of marketing communications?

“This is a massive open-ended question and I spent the first few minutes just trying to scope it.”

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