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Intuit interview notes, page 1 of 7

Large Enterprises

Most of these Intuit write-ups come from Software Engineers (37), then Data Scientists (17) and Machine Learning Engineers (6). The most reported rounds are the technical phone screen (47), the system design round (7), the product sense round (5) and the online assessment (2).

65 interview notes · updated Aug 2026

Intuit·Technical Program Manager·Hiring Manager Screen

Apr 2026
Interviewed for a TPM role at Intuit, just the one behavioral question worth noting here. Pretty standard screen vibe but this particular question tripped me up more than I expected.
  • What is your greatest weakness, and why do you believe it won't negatively affect your performance in this role?

“The second half of this question is what gets you.”

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Intuit·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Interviewed for an ML Engineer role at Intuit and the whole MLOps section was a gauntlet. Seven questions back to back on model lifecycle stuff, and some of them got pretty deep into infrastructure and reproducibility territory.
  • What types of models have you previously trained, and what was the scope of that work?
  • What cloud platforms and ML infrastructure have you worked with?
  • How would you manage versioning when your team is training thousands of models per year?
  • How do you handle dependency conflicts when working across multiple model families?
  • How do you ensure that a model behaves the same way in training as it does after deployment?
  • How do you organize a model registry and structure deployment pipelines for a large ML system?
  • What does a good CI/CD process look like for machine learning systems, and how does it differ from standard software CI/CD?

“Pretty standard opener but they used it to gauge breadth.”

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Intuit·Software Engineer·Technical Phone Screen

Apr 2026
A 75-minute combined coding and system design round at Intuit. Pretty standard stuff but the follow-up on the coding problem caught me a bit flat-footed.
  • Given an array representing a number (e.g. [1, 2, 3]), return the array after adding 1 to the number it represents.
  • Follow-up: how would you handle this if the input array is extremely large, too large to fit in memory?

“Classic plus-one problem, I knew it.”

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Intuit·Software Engineer·Technical Phone Screen

Apr 2026
Had a technical phone screen for a software engineer role at Intuit. One SQL question, pretty focused, felt like they wanted to see if you actually know conditional aggregation or just know how to Google it.
  • Given a coins table and a transactions table, write a single SQL query that returns each coin's name alongside the summed transaction amounts broken out by calendar quarter (Q1 through Q4), plus the total transaction count and total amount across all quarters. Coins with no transactions should still appear in the results with zeros.

“The QUARTER() function was the part I had to think about for a second.”

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Intuit·Data Scientist·Technical Phone Screen

Apr 2026
Intuit Data Scientist interview with a pretty gnarly SQL question that built on a previous problem. The whole thing was a follow-up to an earlier monthly subscriber query and they wanted you to layer in an anti-join to exclude free-tier companies, plus explain the performance trade-offs of different approaches under columnar engines.
  • Take your existing query for monthly new subscribers and year-over-year metrics, then modify it to exclude any company that ever had a free subscription. The exclusion must happen before aggregation, guard against duplicate IDs in the free subs table, and must not accidentally re-include rows through join behavior. Also explain the trade-offs between NOT EXISTS and LEFT JOIN ... IS NULL for this kind of anti-join on a columnar OLAP engine.

“This one stacked a lot of constraints on top of each other and I kept second-guessing the order of operations.”

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Intuit·Software Engineer·Executive / Final Round

Apr 2026
Final round for a software engineer role at Intuit, 60 minutes with a senior engineer and an engineering manager, centered around presenting a project. The main anxiety going in was whether an ML project would fly or whether they'd want to see a full stack app instead.
  • For the project presentation portion of the final round, is presenting a machine learning project (built with a team of interns using real company data) a risky choice compared to a more traditional full stack project?

“This is basically the whole post.”

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Intuit·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Intuit for a software engineering role. The interviewer tailored the question to my resume, which sounds flattering until you're mid-answer wondering if you're being tested on something specific they already know you should know.
  • Design a recommendation system that surfaces content to drive conversions to Intuit's premium product.

“The interviewer said it was pulled from my resume, so I couldn't exactly pretend I'd never thought about this.”

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Intuit·Software Engineer·Technical Phone Screen

Apr 2026
Intuit software engineer interview that was basically one long project deep-dive. They gave you a prompt upfront and then just kept pulling on threads for the whole session. More conversational than I expected, but also harder to fake your way through.
  • Walk through a recent project you built with AI assistance, covering the problem, how you used LLMs in your workflow, the technical decisions involved, how you verified correctness, and what you'd do differently.

“This is a deceptively open question that turns into five separate conversations if you're not careful.”

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Intuit·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM interview at Intuit, product strategy case focused on growing adoption for QuickBooks. Pretty open-ended, which sounds nice until you're actually sitting there trying to scope it.
  • You are a PM at Intuit. How would you increase the adoption rate for QuickBooks?

“I went straight to user segments, which in hindsight was probably the right call but I spent too long there and barely had time to talk about actual levers.”

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Intuit·Machine Learning Engineer·Onsite - System Design / Architecture

Apr 2026
Intuit ML Engineer loop, second session was a case study focused on recommendation systems. The question was meaty enough that I felt like I was building something real rather than just reciting textbook answers.
  • Design an offline personalized promotional product recommendation system that handles delayed and sparse conversion feedback. Walk through each component of your design.

“The sparse feedback angle is where I spent most of my time and honestly where I fumbled a bit early on.”

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