Crack ML System Design Interviews Like a Pro - Part 1
Real Stories, Real Strategies: An Interviewer's Guide to What Actually Works
Let me share something that's been on my mind lately – ML system design interviews. You know what fascinates me? These interviews aren't just technical assessments; they're a test of how well you can structure and communicate complex ideas under pressure. I've been on both sides of the table, and I've noticed that even brilliant engineers sometimes struggle not because they lack knowledge, but because they haven't mastered the art of presenting their thoughts systematically. So, I thought I'd break down my approach to tackling these interviews, sharing real experiences and lessons learned along the way. Let's dive in!
Picture this: you walk into the interview room (or join the Zoom call these days), and the interviewer says, “Design a recommendation system for Netflix.” Where do you even start? This is where having a solid framework saves you. So here is one design framework I propose:
Requirements Gathering
First things first – and I can’t stress this enough – don’t jump straight into solutions! I’ve seen so many candidates eager to show off their ML knowledge that they start talking about matrix factorization or deep learning before understanding what they’re building.
Here’s what you should do instead. Take a deep breath and start with questions. Let me share a real interview experience. I was taking an ML design interview just the other day and I began with an ML design question about search. And the TC immediately jumped into the modeling part. I had to bring them back to the question at hand and make them structure their interview. At that time, I knew then that they were not suitable for a senior role. I know, it’s pretty harsh to judge someone like that, but you can only do so much in a 1-hour interview. So, better to follow a structure. Here is a structure I recommend:
A. Scope Clarification: Start with the basics:
“Are we building this for all Netflix users or a specific region?”
“Should we focus on movie recommendations, TV shows, or both?”
“What’s our main goal – increasing watch time, user engagement, or something else?”
“What type of queries can users do over in a search system? Is it title queries, category queries, or both? Or are they complete queries or partial queries?”
Here’s a pro tip that saved me multiple times: write these points down on the whiteboard (or shared document for virtual interviews). It shows structured thinking and gives you something to refer back to when you need to justify your decisions later.
B. Functional Requirements: Get specific about what the system needs to do:
Input: “What data do we have about users? Just ratings, or do we also have watch history, partial views, etc.?”
Output: “How many recommendations should we show? Do we need to explain why we’re recommending each item?”
Features: “Do we need real-time updates based on what the user just watched?”
C. Non-functional Requirements: This is where you show you think like an ML engineer, not just a data scientist:
Scale: “How many users are we serving? Netflix has over 200 million subscribers – that’s a lot of recommendations to generate!”
Latency: “How fast do recommendations need to be? For the homepage, we probably want recommendations ready before the user logs in.”
Availability: “What happens if our recommendation service goes down? Do we need a fallback?”
Let me tell you a story about why this matters. In one of my interviews, where I specified that we need session-based recommendations for movies and TV series, the TC jumped straight into talking about collaborative filtering algorithms. Ten minutes in, I realized that I had to stop them as they didn’t get that I needed real-time recommendations based on the user’s current watching session. Oops! the TC had to redesign everything. Don’t make this mistake – get these requirements clear upfront.
Architecture Planning

Now comes the fun part – designing the system. But here’s the key: don’t try to design everything at once. Think of it like building with LEGO blocks – start with the big pieces, then add the details.
High-level Architecture: Start by drawing the major components.
Draw these as boxes on your whiteboard. Simple, clean, and clear.
Technology Choices: This is where you can demonstrate your practical experience with technologies. For example: You might come up and say that for our feature store, we could use Feast for feature management, Redis for real-time features, and Apache Cassandra for historical features.
But here’s the crucial part – always explain why: I’m suggesting Redis because we need sub-10ms latency for real-time recommendations, and Redis’s in-memory storage can handle that. Plus, it has built-in data structures perfect for storing user session data. This is perfect!
Evaluation and Metrics
This is often where candidates drop the ball. They design a beautiful system but forget about letting the interviewer know how to measure if it’s actually working! I would always expect someone who comes up for an ML design round to talk about metrics. What are you optimizing for is such an important question. Particularly you might want to talk about these two sets of metrics for sure.
A. Model Metrics:
Tip: Don’t just throw out metrics – explain why they matter: “For our recommendation system, we’ll track:
Precision@K: Because users typically only look at the top few recommendations
Watch-through rate: This tells us if we’re recommending content users actually enjoy
Diversity metrics: To avoid the ‘echo chamber’ problem
The above is such a nice answer if someone gave it to me. We always see these problems in a real-world recommender system, where all recommendations come up alike and we need to have a diversity ranker.
B. Business Metrics:
This is your chance to show you understand the bigger picture. Beyond model metrics, we can track:
Average watch time per user
User retention rates
Content discovery (are users finding new shows they wouldn’t have otherwise?)
I mean what does Precision@K mean to the business, they want to focus on metrics that they can talk about with investors. Average watch time per user increases brings them money so they are most interested. You could create a perfect ML model with perfect metrics and if they don’t move the Business metrics, it’s useless.
Now that you have provided a high-level design with different components and how you are going to evaluate the components, you can choose to take a step back and ask the interviewer if there is a particular part they would like you to focus on. If not, the best way is to move through all the parts starting with:
Data Pipeline Design
Think of data collection like building a city’s water system – it needs to be clean, reliable, and able to handle both regular usage and sudden surges. In data terminology, it should be able to have both real-time data as well as batch data.
Data Collection:
Always consider the various data sources you can have. Do you want to have real-time data based on the system you are building or are you going to do processing in a batch for a non-real time system? Talk about Kafka Streams or Spark processing here. Regardless some things are particularly omnipresent in all Data pipeline designs for systems:
User interactions (clicks, views, ratings)
Content metadata (show details, categories, tags)
External sources (maybe IMDB ratings, social media buzz)
Data Validation/ Cleaning:
This is where real-world experience really helps. We will need to validate:
Data completeness (are we missing crucial fields?)
Data accuracy (is that 10-hour viewing session real or a bug?)
Data freshness (how recent is our user behavior data?)”
Training Infrastructure
Think of your ML Modeling like a factory assembly line – it needs to be efficient, reliable, and most importantly, reproducible. Here you talk about the heart of your ML system - choosing and training the right model. This is where you’ll spend a lot of time in interviews explaining your decisions.
First, let’s tackle model selection. Here’s how I approach it in interviews: Before jumping into complex models, I always start with:
Baseline Models: Simple models that give us a performance benchmark
For classification: Logistic Regression or Random Forests
For regression: Linear Regression or Decision Trees
For ranking: Learning to Rank with GBDTs
Then based on the requirements, we might need:
Deep Learning Models
CNNs for image-related tasks
Transformers for sequential or text data
Deep & Wide models for recommendation systems
The choice depends on several factors:
Data size and type (structured vs unstructured)
Latency requirements (inference time constraints)
Interpretability needs (some domains require explainable models)
Computing resources available
Once you have your model, you would particularly talk about these things:
Model Architecture(If a Deep Learning Model)
Model Input: Features and Feature Engineering
Model Output: Training Labels
Loss Function
Model Metrics
Train/validation/test split strategy
Always write down your training pipeline steps as if you’re writing instructions for someone else. I’ve found this reveals gaps in your process quickly. And always be ready to explain why you chose specific models or approaches - interviewers love diving deep into these decisions.
Deployment and Serving
This is the unsung hero of a ML design interview as most interviews finish at the previous stage. But if you have time in your ML interview talk about:
How you would serve your models—real-time inference, batch predictions, streaming inference.
How you would deploy your model. You can deploy a model on the cloud or the device depending on the use-case.
You can also talk about caching prediction responses, and asynchronous handling of model requests in cases the model takes a longer time based on the exact system.
Talk about System Latencies, Model Drifts, Data Quality, etc.
Overall, it might help to have this structure saved somewhere in your mind. I would suggest saving the chart at the start of this chapter and practicing with it.
Remember, in ML system design interviews, there’s rarely a single right answer. What interviewers want to see is your thought process and how you handle trade-offs. Don’t be afraid to say things like “We could do X or Y here. X would be better for latency but more expensive. Given our requirements, I’d choose X because…”
And there you have it – a comprehensive roadmap for crushing your ML system design interviews! But here's the thing: this framework isn't just about acing interviews. It's about developing a mindset that'll serve you well throughout your career as an ML engineer. In the real world, you'll rarely build systems from scratch, but you'll constantly need to make design decisions, evaluate trade-offs, and explain your choices to stakeholders.
Remember, the key isn't to memorize this framework but to understand the why behind each component. Every system is unique, and what matters is your ability to adapt these principles to different scenarios. Start practicing with various use cases – recommendation systems, search engines, fraud detection – and you'll start seeing patterns emerge.
And hey, if you stumble in your next interview, don't sweat it. We've all been there. What matters is learning from each experience and continuously refining your approach. Now go out there and design some awesome ML systems!
Now, I know what you're thinking – "This framework sounds great, but how does it play out in a real interview?" Well, you're in luck! In the next article, I'll walk you through this exact framework using a real-world ML system design problem. We'll tackle it step by step, just like in an actual interview, complete with all the trade-offs and decision points you'll need to navigate. It'll be like you're right there in the interview room with me – so stay tuned! Trust me, seeing this framework in action will make everything click. So stay tuned!
That was a great read.
Waiting for the next part .. God speed !
Nice Framework! The best part is “the key isn't to memorize this framework but to understand the why behind each component.” that's a real mentorship!