system design interviews are a core part of the hiring process at companies like Meta, Apple, Reddit, Amazon, Google, Snap, and many others.

These interviews vary widely — some focus more on software architecture, others on problem framing or ranking systems, and communication styles and expectations can differ significantly between teams.

Understanding these differences and learning from each approach reveals valuable insights into what makes a strong interview performance. Each variation highlights different skills: translating business goals into ML solutions, handling ambiguity, or staying calm under pressure.

Compared to software engineering system design, there are far fewer structured resources available for ML system design interviews.
This post brings together a general framework, common pitfalls, and practical tips, along with a curated set of resources to help you prepare and excel in your next ML system design interview.

What do these interviews try to test?

I personally very much like the design interviews — they are more interesting, unpredictable, challenging, and practical compared to generic machine learning, behavioural, and coding rounds. Depending on where the conversation takes you, design interviews might cover every other type of interview and provide indicators for whether the candidate has done any actual work and understood the rationale, the scale and complexity of the problems they’ve solved in the past as well as their seniority level.

Design interviews test the depth and breadth of your core skillset through conversations about design choices and trade-offs between different architectures. Generally it is tested through your ability to demonstrate the following:

• Control and lead the conversation: Strong candidates guide the discussion rather than passively following prompts. This means setting a clear structure, outlining your plan early, and proactively driving the conversation toward meaningful technical and product decisions.
• Questions that you ask and the scale: The quality and scope of your questions reflect your experience. Thoughtful clarifying questions show that you understand the business context, system constraints, and data dependencies before jumping into solutions.
• Nuances and the high-level picture that you are translating through your answers: Great candidates move fluidly between details and abstraction. You should be able to discuss technical mechanisms while tying them back to user experience, business goals, and system performance.
• Behavioural aspects — how you react to new information and defend your ideas: Interviewers pay close attention to how you respond to ambiguity or pushback. Staying calm, adapting your solution thoughtfully, and justifying your design choices with reasoning instead of opinion demonstrate maturity and collaboration.
• Coding — your ability to discuss nuanced implementation details: Even though design interviews are not pure coding rounds, the best candidates can dive into specific implementation aspects — such as data pipelines, model serving strategies, or optimization methods — when prompted, showing that their ideas are grounded in practical engineering experience.

Expectations

For various levels of seniority a company would have different expectations from your performance at the design interview for exactly the same question.

For juniors: the expectation is that you are well-versed in the technical details of the algorithms you are proposing and not as much the business aspect of the problem you are solving.

For mid-senior engineers: the expectation shifts toward demonstrating not only strong technical depth but also system-level thinking and an understanding of how your design decisions impact scalability, latency, and overall product goals. You should be able to translate ambiguous product requirements into clear ML formulations, discuss trade-offs between different approaches, and reason about data collection, experimentation, and model evaluation strategies. Communication becomes increasingly important at this stage — interviewers look for engineers who can guide the conversation, ask clarifying questions, and balance technical rigour with practical feasibility.

For Staff+ levels: the expectations spans to technical depth and breadth, business impact, and broader consideration of the feature or model deployment and production tracking. At the higher levels, you are also expected to drive the conversation independently, where the interviewee is expected to talk 95% of the time, rarely turning to the assessor for targeted clarification questions. At this level, you are also expected to be aware of the trade-offs of the paths you are taking — very often, there are no right or wrong decisions, there are decisions that have pros and cons.

Holistically assessors are collecting signals — from the Meta interview guide:

• Problem Navigation: Can you visualize and organize the problem-solution space? Can you connect the business context and needs to ML decisions?
• Training Data: How would you identify methods to collect training data? How do you look at the constraints / risks with a proposed method?
• Feature Engineering: Can you come up with relevant ML features for your model? How do you identify important features for the specific task?
• Modelling: How do you explain modelling choices? Are you able to justify the decision to use a specific model? Can you explain the training process? Can you anticipate risks and how do you mitigate those risks?
• Evaluation & Deployment: Can you design consistent evaluation & deployment techniques? How do you justify and articulate your choice of metrics to track?

Interview Setup

In a typical machine learning system design interview you would have from 30 to 40 minutes to solve it. In this time you would need to explore the space, propose the core of the solution, discuss training, testing, and deployment, and potentially deep-dive into a couple of components.

Structuring your interview

The best machine learning system design interviews unfold like a story — they have a clear structure, logical flow, and a sense of progression. While every story (and every interview) is unique, shaped by your experience and the specific problem at hand, strong answers follow a consistent framework. A typical ML system design interview can be structured as follows:

1. Business problem understanding and clarifying questions — who is the end user, how much requests are we expecting, is the model used in downstream tasks, etc.
2. Machine Learning task formulation and offline and online metrics — what exactly we are solving and how we would measure the success.
3. High-level architecture — defining the flow of the model early on to make sure that the interviewer has the full picture, this will help with the time-management later.
4. Data — how do we collect the data, where is it coming from, do we need to do any data pre-processing, filtering, cleaning, balancing, re-structuring?
5. Features preparation — do you need to do any dimensionality reduction, perhaps, get embeddings first? May be you need to align modalities? How would you mix data from various sources?
6. ML modelling — deep dive into the architecture, losses, optimisers and model specific trade-offs.
7. A/B testing — how would you set up an experiment, what would be the population distribution, what would be the treatments?
8. Deployment — online learning, MLOps, model optimization, monitoring, logging, etc.

A list of questions my colleagues and I encountered

• Design a video ranking system.
• Design a place recommendation system.
• Design a weapon sale detection system.
• Design a user bug reporting system.
• Design a Spotify recommendation system from the most popular tracks in the past hour.
• Design a system to evaluate insurance claim size from the picture(s) of a damaged object.
• Design a fine-tuning pipeline for a large language model for a chat-bot.
• Design a system for bank transaction fraud detection.
• Design a face-swap lens.
• Design a community based message moderation system.
• Design an ad recommendation system for Instagam.
• Designing next post logic for FB news.
• Design a model for translation.
• Design story feature in Instagram.
• Design a system that would translate videos to a target languge. 

Preparing for design interviews

Be comfortable with machine learning fundamentals. While preparing this article, I found this interview Q/A book for machine learning positions, which has a solid list of questions with answers to them. Another excellent book, Machine Learning Interview preparation book, has a lot of good interview behaviour and salary negotiation sections and technical questions. Also, I found this blog that summarises feeds from glassdoor on machine learning interviews. And a comprehensive list of common ML questions.

Read as many blogs and case studies on ML system design as you can. I cannot stress more, how important that is. It is a great way to learn about new areas of machine learning. After reading 10–20 of these you start finding common patterns and areas that are important to focus on for each of the ML domains. This is a list of the ones that I would recommend.

Papers:

Books:

Watch as many example videos. While there are not as many resources for ML, system design interviews for software engineers would be helpful to understand various interviewing styles and what is expected.

Prepare the list of questions and a rough structure for your design delivery. A few examples of such lists are:

Do as many mock interviews as possible. Luckily there is a plethora of resources to do that!

• https://adplist.org/: website where you can find mentors, some of which also do practice interviews (me included).
• https://interviewing.io/: a great platform that takes interview quality very seriously — with each interviewer being very experienced and needing to pass a very high bar. The platform also has a plethora of useful articles and recordings of real interviews — check it out!

Do machine learning system design courses. 

Pro Tips

• Make sure you explore the problem. Ask questions, make sure that there are enough questions, to make sure that you collect the right data, big picture, and not miss the scale. Not knowing what you are trying to solve or solving a wrong problem would reflect badly on you.
• Drive the conversation. If you wait for the interviewer to drive the conversation, it simply shows a lack of initiative, and you always want to show the opposite. You should be speaking most of the time. 
• Practice drawing. Prior to the interview, practice drawing a few design diagrams on Excalidraw. This will make it a bit easier during the interview: if you can save a few seconds of figuring out how to draw a circle, that’s a few seconds more you can spend on brainstorming.
• Take notes as you study. During prep, always take notes about what you’ve learned. Don’t just copy-paste, write things in your own words as a way to probe your understanding.
• Perceive the interviewer as a junior. Do not think about the assessor as an interviewer, think of them as a junior engineer to whom you are explaining how the final pipeline should look.

Frequently Asked Questions

What do I do if I don’t know the answer to the problem?

That’s the whole point — the interview is testing your ability to face an unfamiliar and ambiguous problem and navigate your way to a solution. Don’t stress if you feel like the solution is not coming straight away. Gather the requirements, the constraints, and think about the most simple thing that would crack it, and then add complexity as you go.

What if I am not familiar with the area?

Interestingly this is more common than not. If you have been specialising in generative models and interviewed with Meta, you have most likely been asked about ranking in one way or another. While the areas differ, there are still common flows for cracking these problems and common machine learning fundamentals to build on. While you might not know the area, your experience might bring a fresh perspective. There is no right or wrong — the interviewer is interested in your thinking process and overall understanding of the machine learning area. However, the best way to not fall into this trap is to prepare for the company you are interviewing for.

What if I am running out of time?

It does happen very often — after all you’d have a big and unfamiliar problem to solve. Some interviewers don’t even expect you to finish and would want you to focus on certain parts more than the others. However, generally, if you notice that you are falling behind the schedule you have several options. First is to ask — one is to ask the interviewer, explicitly say that you see that you are running out of time and if they want you to focus on a specific section; another one is to summarise what you have already talked about and move on prioritising the remaining parts. As obvious as it sounds, the key to not falling into the trap is to practice and allocate exact time frames for each section.

What if I have finished earlier?

It’s your chance to cover in more details the sections you feel are important. You can also seek help from the interviewer and ask them if they have questions. Generally, good things to cover would be corner cases, practical considerations, and managing the lifecycle.

What if I get stuck?
Getting stuck is normal — you are under pressure to solve a complex problem that often takes several engineers to solve. The first thing is to stop hitting the wall. Second: verbalise this to the interviewer — we are all humans and verbalising that you are stuck takes off the stress of pretending that you are not, and hence freeing up mental resources. Third: repeat what you have gathered and built so far. Fourth: if you feel like you are not moving at all, start working on a different part of the pipeline. In any case an experienced interviewer would pick up the cues and will guide you out of a dead end. 

How do I use the whiteboard?

Most interviews use excalidraw. Make sure to open it before the interview and learn the interface — for example, how to put text in the shapes and draw arrows. Overall, as you explore the problem, take notes — what is the scale of the problem, what are the requirements, etc. Make sure that you are not typing all your answers word by word — it’s too time-consuming, but that the notes are self-sufficient, as interviewers might go back to these notes when writing feedback. Overall, notes are important because they also help you structure your response. One thing that you could do is to explicitly outline all the sections that you are planning to cover, making it easier for you to follow through. Ask the interviewer what they would prefer: notes or drawing when it gets to the design part. For the drawing part, you might want to make sure that you are focusing on the big picture first — drawing large dependencies and the flow, and jumping into details only if necessary.

Summary

In the end, mastering ML design interviews isn’t about memorizing patterns — it’s about developing structured thinking, curiosity, and the ability to connect business goals with technical solutions. Every interview is a rehearsal for real-world engineering, where trade-offs, ambiguity, and communication matter as much as the model itself. 

To turn preparation into progress, start small: pick one design question from the list above, time-box 40 minutes, and talk through your solution out loud. Then review what went well and what felt unclear — that reflection loop is where real improvement happens. Build a habit of doing one mock interview a week, refine your frameworks, and share your learnings with others. 

Over time, your answers will sound less rehearsed and more like what they truly test for: practical, confident, system-level thinking. You’ve got it!

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