Mastering Machine Learning Design Interviews: Essential Strategies

Machine learning candidates often fail due to poor problem understanding and time management, not a lack of knowledge.

### Mastering Machine Learning Interviews: Six Essential Stages for Success
* 🚀 "70% of machine learning engineering candidates I interviewed failed in the same round, and the thing is it's actually **really easy to pass**."
* 💡 Having over a decade of experience at companies like Adobe, Twitter, and Meta, I identified **six key components** that can significantly enhance your chances of **success in machine learning interviews**.
* 🕒 It's crucial to understand that spending hours before an interview on technical specifics like loss functions may not be as beneficial as **grasping the overall interview dynamics and question comprehension**.
* ✈ Just as I learned the importance of **feel over technicalities** when piloting a plane, candidates must focus on **understanding the problem**, **managing time effectively**, and **avoiding unnecessary clarifying questions**.
* 🛠 The first stage involves creating a **high-level system diagram** that addresses the main requirements of the problem, while the subsequent stages delve into **data considerations, modeling metrics, and operational strategies**.
* 📊 In the critical **15 to 16 minutes** of the interview, candidates should demonstrate their knowledge of **machine learning concepts** relevant to the scenario, focusing on **classification or regression**.
* 🌙 Finally, the last stage allows candidates to **impress the interviewer with their expertise** and to **ask insightful questions about the company**, ensuring a **well-rounded interview experience**.
* 📚 To prepare effectively, candidates should **identify their weak spots**, utilize available resources, and conduct mock interviews to **solidify their understanding and readiness** for the ML design interview.

70% of machine learning engineering candidates I interviewed failed in the same round, and the thing is it's actually really easy to pass. All you need to know is what you're doing. Having worked at companies like Adobe, Twitter, and Meta, I spent the last 10 years on

both sides of this interview and realized it all comes down to acing these six things. So in this video, I'll reveal what kinds of questions I had to answer to land a job at Meta, share my system for answering any machine learning design questions your

interviewer might throw at you, and I'll even give you one stellar strategy to prepare in less than a day. But before I can share my system with you, you first need to realize that you're preparing for your interview all wrong. If you're spending the hours leading up to your

interview reading up on loss functions, then you're wasting your time. The thing that trips up most candidates has nothing to do with their ML knowledge. I played a lot of Microsoft Flight Simulator when I was in college. I know where every button and instrument gauge

on the Cessna 150. Recently, I had a chance to take the yoke of an actual plane and, uh, well, my friend had to take over to keep us both safe. The things that keep you from landing your dream job are not about the buttons but the feel of the

interview. Specifically, insufficient understanding of the question, confusing your interviewer, and poor time management. And that's where my system comes in. Using the system allowed me to prepare for my interview at Meta in half a day, and the interviewer was so impressed that I got an up-level from

The senior position I was interviewing for, to staff interviewing at Fang companies, especially at senior levels, is like shooting for the moon. Say you were actually tasked with that, I give you unlimited funding and say, "Fly to the moon. It's up there." And vaguely point up

in the sky. This is sort of what the ML design interviewers do. They give you a generic question and then sit back watching your spacefaring dreams sail by. Being off by even half a degree on your course to the Moon already assures you will never get there if you don't

fully understand the problem. You might overcomplicate the solution or fall short of addressing it fully. Make sure you take 5 minutes or so to really understand the question. You sound a lot more competent when you make assumptions than when you pester your interviewer

with questions. So limit the amount of clarifying questions you ask. And instead of panicking about details like exactly how many users does Facebook have, just say "Facebook has about 3 billion users." This next stage should take you about 6 minutes, but if you neglect it, you will

spend much more time in back-and-forth explanations with your interviewer. Sadly, too many ML candidates fail because they use this stage as the opportunity to dive deep into rabbit holes, showing complete inability to do the high-level design and wasting precious time, making it

impossible to recover. Instead, you need a rocket blueprint. You need to be very deliberate about building a very high-level system diagram with barely enough detail to address the main requirements of the problem. So instead of XGBoost, you need to think "model" and leave the

specifics to a later stage. Doing this R avoids most miscommunications between you and your interviewer. Be familiar with whiteboarding software you will use in the interview. You do not want three out of six minutes in this stage to go to figuring out how to draw a square.

While having a good plan goes a long way toward clearing this interview, this next part is the second largest component of your score. Everything you have done in the first 11 minutes is a great setup for a successful interview, but this stage is where ignition happens, the

rocket lifts off the launchpad. The objective of the next stage is to pick up speed and not experience a rapid unscheduled disassembly. I know I have experienced a few of those in my interviews over the years. How about you? Let me know in the comments if you have

done the pre-work. This is the fun part, of course. I'm talking about data considerations, and there are a lot of data questions to consider. To get this space, you must at least understand what your labels are and where they will be coming from. Understand what your

features are and how to translate them into numbers. Understand how to normalize those numbers. Understand how to split your data set. Are your data imbalanced? They almost always are. How will you address that? This part should take you about 8 to 9 minutes. Note that this is a lot of

ground to cover in a short amount of time. You should not discuss every feature, just a few representative ones. Congratulations, you're in low Earth orbit, but this next boost will get you all the way to the moon. This section covers the most important thing your interviewer is

Looking for the next 15 to 16 minutes are critical. This is no longer time for clarification; you need to make the most out of your time. Your task in this stage is to talk about modeling metrics and training, as well as overfitting and

other issues that may come up, like cold start and time travel problems, and propose solutions to them. This is where you demonstrate your machine learning knowledge targeted to the scenario your interviewer actually cares about. While this is a lot of material and you may be asked a wide range of

questions, don't panic. At its core, almost all of machine learning is either about classification or regression, and in fact, many problems can be solved with either. Once you define your problem as one of those, there are standard paths that will keep you safe. Congratulations, you're now as

successful as Apollo 13. Wait, no, that's a bad one. Apollo 8, both were piloted by Jim Lovell. Anyway, Apollo 8 is a pretty good comparison here, though. It was a test mission that went around the Moon without landing. Earlier in the Apollo program, if you're early in

your career, you may already have it in the bag, but just to be sure, or if you're looking at more senior jobs, let's land on the moon. Stage five: you have 5 minutes to knock the interviewer's socks off. Your one small step... interviewers will

sometimes focus on how to launch and operate a machine learning system in production, but if you are an expert in training parallelization or online evaluation techniques, this is your time to shine. There are often multiple ways to solve the problems you will find in this section. At least once, you should come up

with trade-offs between two or more reasonable solutions and make strong recommendations for one of the wait I thought you told me there were six stages indeed while the rest of the world was rooting for Neil Armstrong to step on the moon I am sure his family

would not have liked the mission as much if he didn't land safely stage six is just for you it would be a big bummer to break into the company that breaks your will to be in the industry so use the remaining time to ask the interviewer

questions about working at the company you only have time for one or two questions here so make them count if you follow these steps with more or less suggested timing you will ace your ML design interview no matter what question is asked but you want to know what

questions are usually asked don't care well I have some good news for you the great majority of the questions for machine learning design interviews come from just a couple of areas of machine learning and even if these are not your area you can get up to speed on these by

reading just a few papers and blog posts Linked In the description most big tech companies focus on only two types of questions for their generalist positions these two question types are popular because they test a broad swath of machine learning knowledge and the interviewer can dive into a lot of

places to understand what you know the second most common type of question for this interview is harmful content or inappropriate content something like detecting firearm in an Amazon listing or nudity in a social media post this is the type of question I got asked that

sealed the E6 meta offer for me, but even more common is the question I asked as an interviewer in big tech: design a recommender system. Something like a Twitter timeline or Amazon product recommendation. For specialized roles and in smaller companies, the questions tend to be very targeted to what your role will be. If you are a vision specialist, expect to design a vision system. While machine learning is a very deep field, a handful of areas are over-represented in ML design questions. However, you should never assume that you will get one of those problems. Instead, let's talk about

how you would study so it doesn't matter what questions you get asked. The machine learning system design round is one of the many interviews you're preparing for on top of all the other things you're doing. How do you prepare for it fast and without sacrificing sleep? Luckily, in

addition to my experience applying to Meta, I have since helped dozens of people get into FAANG companies at levels from new grad to principal engineer with this advice. And you already know the first step from earlier in this video. There's an apocryphal story of Charles

Proteus Steinmetz, a renowned electrical engineer who was called to fix a generator for Henry Ford. After marking a spot with chalk and instructing the engineers to make specific adjustments, he charged $10,000 - $1 as he said for the chalk mark, and $9,999 for knowing where to place it. Understanding the six stages of the interview is the absolute prerequisite to doing this efficiently. Pick one of the questions I mentioned in this video and go through how you would answer each stage in brief. If you know offline metrics really well,

just check them off your list this is your $9,999 task figure out where to place your efforts then you read up on topics you identified as weak spots utilize resources in the description when you need them skip the ones you already know at this point you have learned to swim

by reading a book well remember when you tried to learn how to swim using the internet I did learn how to swim on the floor the skills are transferable I just have no interest in going in the water Kudos however a real pool full of water may present some

different challenges whatever else you do plan to do two full mock interviews you can do a paid mock just be very careful to look through reviews or just buddy up with a friend who is at a similar level I will walk you through how to conduct a mock interview in a

future video on this channel if you apply what you learned in this video do a few hours of additional research to fill the gaps in your understanding and do two mock interviews you will be perfectly positioned to ace your ML system design interview getting through the ML Design Round is only part of what

it takes to have a stellar career in Big Tech to learn more about what it takes to be an MLE hit subscribe so you won't miss my future videos

This ML Design Interview strategy got me into Meta

MLEpath

Common Mistakes in Machine Learning Engineering Interviews

70% of candidates fail due to easily avoidable mistakes.

Success hinges on mastering six key areas.

Focusing on minutiae (e.g., loss functions) is ineffective.

The key is understanding the interview's 'feel', not just technical details.

Critical issues are understanding the question, avoiding confusion, and managing time effectively.

A Six-Stage System for Acing the Interview

A system enabling Meta interview preparation in half a day.

ML design interviews are analogous to vaguely defined space missions.

Spend time understanding the question; limit clarifying questions.

Create a high-level system diagram, avoiding unnecessary detail initially.

Focus on a high-level system design before delving into specifics.

Data considerations are crucial (labels, features, normalization, data splitting, imbalance).

Modeling, Metrics, and Addressing Potential Issues

Discuss modeling metrics and training, addressing overfitting and other problems.

This stage demonstrates specific ML knowledge relevant to the scenario.

Many problems can be solved using classification or regression.

Production System Considerations and Trade-offs

Discuss launching and operating an ML system in production.

Highlight expertise in training parallelization or online evaluation.

Present trade-offs between different solutions.

Ask insightful questions about working at the company.

Most questions fall into a few key areas of machine learning.

Big tech companies focus on generalist questions (e.g., recommender systems).

Harmful content detection is another common question type.

Specialized roles in smaller companies have more targeted questions.

Prioritize understanding the six interview stages.

Identify weak areas and focus study efforts accordingly.