Intuit India — Full Time Interview Experience (wrt MLE)
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Intuit India — Full Time Interview Experience (wrt MLE)

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Intuit is an American business company that specialises in financial software. Its products include — Turbotax, Mint, MailChimp and Quickbooks. Most of its customer base is in the US.

Intuit India is known for its great work culture and employee centric policies like flexibility of deciding your own deadlines, special refreshing days for employees, lots of gifts and goodies, and a very approachable and encouraging leadership! (and much more!)

It is also recognised as the #3 Great Place to Work (#1 in Tech) for the year 2021, by the Great Place to Work? Institute (India) and The Economic Times!

In this blog, I am sharing about—

  • My Interview Experience with Intuit India for a Full-Time Machine Learning Engineering Role.

Before delving into my experience, Let me share how I got the interview opportunity...

How I Applied!

I usually post on my LinkedIn whenever I receive some R&R from my employer's. And this is something which I believe had caught the attention of one of HR's and they reached out to me with an interview opportunity via LinkedIn messaging. So, post the intimation, my initial [technical] screening round was scheduled over a Zoom Video call very quickly and below is the gist of the same,

>>> First Technical Screening Round, 15th December, 2021 ~60 Minutes Round

  • In-depth explanation of the projects you had worked on (both personal + at your current organisation) is very possible
  • Typical ML based questions, general ML model life-cycle, question around model deployments, config managements, hidden technical debt involved etc
  • There were questions around Apache Spark, like explain the general architecture, how it works, some commonly used optimisation tricks, can you share how to do this (this is some task) followed by questions around Python etc
  • And finally I was asked to design a website like Stack-OverFlow with the primary focus on the dB schema you opted for and discussions around the same + where ML can be leveraged here (I had suggested type-ahead completion, you can also suggest something else like sharing relevant posts as newsletters (hot questions for the tags that you follow or had used in past etc)

Interview Experience

Let me say this upfront, The interviewer's (all the 4/5 people involved + the HR's) are super super friendly, it's rare to get such interviewer's actually! Thanks for being so cool! Hats Off!

There were 4 rounds (all scheduled over a zoom-video call) after the initial screening round and below is the (broad) summary for each one of them. For me, I got them scheduled on the same day i.e. straight 4-5 hours of grilling with breaks wherever necessary! Due to year end refresher's break and few people from the panel being tested positive for Covid, this got delayed for me and happened after quite sometime and frankly speaking, I wasn't expecting them to reach back either, but they did!

>>> Second Technical Round, 3rd February, 2022. (Business Title -: Craft / Design Round + Q/A's) ~75 Minutes Round

In case you have never heard about Craft Rounds, let me share what it stands for first. Craft Demo Round is something like they will give you a mini assignment / side project (2-5 days ahead of the interview day) and you have to design it yourself. For example build a search system similar to Swiggy's where people can submit the dish name and you have to find the closest restaurant, the best restaurant in some K miles radius etc. So, to build something like this on your local, you need to have a DB server, Use some framework to build that app in a micro-service architecture probably, don't forget to have some test cases / logging etc! Also have REST end points for each components of that project (if applicable)

What I feel they were testing in this round are Design Patterns, Coding Standards, Problem Solving / Understanding, Your Thinking Style etc. NB It's a first time interaction with the whole panel, so try to do your best in this round!. You can also watch this video to know more!

Be well prepared because you can expect grilling from the panel on every design choice you have made, lot of Q&A's can be easily expected here, be prepared, think twice before answering them, especially to the questions which could be "out-of-the-box" type!

>>>Third Technical Round, 3rd February, 2022. (was scheduled for 45 minutes but went for ~120 minutes!)

Questions that I was asked revolved around the following very broad themes primarily,

  • Explain one of your projects that you have selected, talk about your role in it (in depth), talk about the technical challenges that you had to overcome, how you did that, why was it a road block for you? and questions around that project that you have selected
  • Then you can expect some questions around Data Structures + Programming Language of your choice + General OS
  • Then they can ask you some general ML questions like your favourite statistical algorithm (be prepared for any question around it), how would you go about with the problem statement if it's not clear etc
  • Then there were other discussion on different topics which I guess will vary from an individual to an individual, like I was working on RecSys (and A/B testing), so you gotta talk about them as well if you had mentioned it, what are the well known ranking metrics, how do you decide KPIs, how do you do A/B testing, near real-time / offline architecture difference etc
  • Last but not the least, you can assume that there will be questions around Spark (you have to explain / write some snippet for the task they provide) + SQL based questions (you have to explain / write the queries invovled)

>>> Fourth Technical Round, 3rd February, 2022. (~30-35 minutes)

Post the previous rounds, I feel that questions started becoming a bit less technical (i.e. a bit less on tech side and more on evaluating your team skillset(s), an individual's learning style, pace etc) and more about how did you do this? What are you working on at your current org? How do you handle a scenario like this (some scenario goes here)? Again some deployment queries (docker, server-less etc), PySpark and A/B testing questions were asked in this round as well and other things that's mentioned on your CV.

>>> Hiring Manager Round (Last Round), 3rd February, 2022. (~40-45 minutes)

Some things that I can recall,

  • Tell me the differences b/w SDE and ML, ML and DS etc (The HM provided a detail diff as well!)
  • Then some situation based questions were also asked, they wanted to know your whole career history basically as well and pickup such situations and how you dealt with them as well.
  • How do you learn new things?
  • How do you handle conflicts regarding designing some system or some idea or using some package within your team / seniors?

Result — After this round, I heard my selection result within 2-3 business days time (real quick this time!). And I finally received a full-time MLE-II role from Intuit India.

My observation — Intuit focuses on many things apart from DSA as well! So brushing up on what Intuit does, Core CS subjects, Resume (work / self projects, technical skills, internships etc) before the interviews will be very useful! And be confident when you will be talking about your craft project, after all it's the very first round!

That's a wrap for this post!

NB They are always hiring, so feel free to browse through their career's website and apply accordingly! Feel free to reach out to existing employees if you wanna seek a referral or some role-specific interview tips etc, I am sure they can help you with it!

Best,

Aditya.

LinkedIn

Mark Moyou, PhD

Sr. Data Scientist | GPU Poor Advocate | Podcaster

2 年

nuff respect ??

Prashanth Seshadri

Director AI, Analytics & Data. Hiring Data Scientists, ML Engineers, Data Engineers & Technical Analysts

2 年

Superb article, this is how we hire and if anyone is interested in opportunities at Intuit, reach out to us or Aditya Soni

Bibek Rauth

Principal Data Scientist at Eli Lilly and Company | Ex TCS | Machine Learning |Predictive Modeling| Statistics | Python

2 年

I don't understand when you say MLE and there is no question related to Classical ML and Stats.

回复
Sahil Pahuja

Making machines intelligent | LLM Engineering

2 年

This doesn’t look like a job interview for MLE

Aditya Soni

Data | Software & Machine Learning Engineering | Kaggle Competitions Master

2 年

Please share it with people who are in your network who can benefit from the same! Let's make the playing field even! Thanks!

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