Intuit Interview Questions — Page 5

Intuit·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Intuit for a software engineer role. The whole thing was one big open-ended problem about building a future best-seller prediction system for an e-commerce category page, end to end. Pretty intense scope for a single session.

Design a system that predicts future best-sellers in a product category for a user-specified time window (anywhere from 24 hours to 90 days out), returning a ranked top-K list with low latency on high-traffic category pages.
How would you define 'best seller' for ranking purposes: units sold, revenue, or some blended demand score? And does the business care about absolute volume or rising momentum?
Should the ranked list be global across all users viewing a category, or personalized per user? How does that choice affect your precompute strategy?
Walk through the write path: how do raw orders and clickstream events become model-ready features and eventually materialized forecasts?
How do you serve a (category, window) request under 500ms p95 given tens of millions of items and bursty traffic?
How do you handle cache invalidation and what's your fallback if the cache misses or a downstream service times out?
How would you evaluate this system, both offline before launch and in production?
An item goes viral at 2pm and starts selling fast. Your forecast refresh is nightly. How does your system surface it before the next batch run?
A user requests a 90-day forecast window. Error compounds over time. How do you communicate uncertainty in the ranking, and would you cap the window?
If you had to incorporate an LLM or agent pipeline to improve cold-start accuracy using reviews and external trend signals, exactly where in the architecture does it live, and how do you keep it off the latency-critical path?

“This one is massive.”

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit DS interview focused entirely on a single product scenario: a pricing page redesign that introduces an annual subscription option. Four connected questions building from impact sizing through experiment design to quasi-experimental fallbacks. Pretty thorough for one case.

Before running any experiment, how would you estimate the business impact (positive and negative) of adding an annual pricing tier to a page that previously only offered monthly plans?
Design a metric framework for this launch: what are your primary success metrics, diagnostic metrics to explain movement, and guardrail metrics to catch unintended harm?
Walk through how you'd design the A/B test for this change: randomization unit, key segments, sample size and power calculations, duration, and how you'd handle pitfalls like SRM or novelty effects.
If an A/B test isn't possible due to engineering or policy constraints and the feature launches to all users at once, how would you still measure impact credibly?

“I started with the obvious upside: annual plans lock in revenue and reduce churn.”

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Intuit·Software Engineer·Technical Phone Screen

Jun 2026

Intuit software engineer interview that mixed a deep technical dive on a past project with a surprisingly philosophical conversation about AI. The AI portion caught me off guard in terms of how conceptual it got.

Walk me through your background, then pick one project and go deep on your role, the technical decisions you made, the trade-offs involved, and the impact it had.
Where do you think AI should and shouldn't be applied in software products?
What does 'AI-native application' mean to you, and how is it different from a traditional app that just bolts on AI features?
Give concrete examples of products where AI is a good fit versus a poor fit, and explain your reasoning.

“I picked a project I thought was safe and familiar, but the follow-up questions went way deeper than I expected on the trade-off side.”

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Intuit·Software Engineer·Technical Phone Screen

Jun 2026

Intuit software engineer interview that went pretty deep on LLM reliability, specifically around getting APIs to return clean JSON without any extra noise. One question but it had a lot of layers, felt more like a mini system design session than a typical coding screen.

You need an LLM API to return strict JSON only. How do you structure the prompt and request so the model reliably produces JSON with no extra text? Walk through message roles, schema or tool-calling constraints, parameter choices, runtime enforcement, and how you'd evaluate and iterate on the solution.

“This one sprawled in a way I didn't expect.”

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit data scientist technical screen, one question, all SQL. The problem was a cohort retention query and it went deeper than I expected for a phone screen.

Given a users table with signup timestamps and a user_events table with event timestamps, write SQL to compute monthly cohort-based retention. Return cohort_month, month_number (0, 1, 2, ...), cohort size, retained users, and retention rate. A user is retained in month N if they have at least one event in the calendar month that is N months after their signup cohort month. Ignore events that occur before the user's signup timestamp.

“The month 0 case tripped me up first.”

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Intuit·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a software engineer role at Intuit and got a bracket matching problem, which sounds easy until you're actually writing it out under pressure. Pretty standard technical screen, nothing too wild.

Given a string containing only bracket characters, write a function to determine whether all brackets are properly matched and closed in the correct order.

“Classic stack problem.”

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit data scientist interview with a meaty ML evaluation question. The whole thing was one technical problem but it had enough layers to keep you busy for a while.

Given a dataset of binary classification outputs with actual labels, predicted labels, and a confidence score per record, write code to compute TP, FP, TN, and FN, then calculate precision, recall, and F1. Also explain how you'd derive predicted labels from confidence scores using a threshold, how that affects the metrics, and how you'd handle division-by-zero edge cases.

“This looked manageable at first and then the threshold part tripped me up a bit.”

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Intuit·Software Engineer·Technical Phone Screen

Jun 2026

Quick coding screen for a software engineer role at Intuit. One question, pretty straightforward, nothing that'll keep you up at night.

Write a function that takes a directory path and returns the maximum file size found anywhere under that directory, including subdirectories.

“Recursive file traversal.”

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Intuit·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a software engineer role at Intuit and got a graph problem involving business relationship data. Had the right general idea but couldn't close it out under pressure, which was a rough way to find out my coding practice had some gaps.

Given business relationship data from a financial platform, implement a function that finds the degrees of separation between two businesses using the shortest path between them.
Why would you choose BFS over DFS for finding the shortest path between two nodes?

“The problem description was intentionally vague on argument and return types, so I had to ask clarifying questions before writing anything.”

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Intuit·Software Engineer·Technical Phone Screen

Jun 2026

SQL-heavy technical screen at Intuit, one question but it had a lot of layers. Window functions plus LISTAGG plus normalization all in one problem felt like a lot to hold in your head at once.

Given a transactions table with a status column and a failure_reason column, write a query that produces a summary with: total transaction count, total amount, and an aggregated list of normalized failure reasons ordered by frequency, all grouped by status.

“This one took me a minute to break down.”

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