Meta System Design and Product Architectural Design Interview Guide

At Meta, the design interview is essentially a compressed version of the design conversations that happen every day across teams working on Feed, Messaging, Video, and Ads. For forty five minutes, you are dropped into a discussion that mirrors how engineers reason about systems in motion, with the emphasis shifting depending on whether the role sits closer to infrastructure or closer to product. Some conversations gravitate toward questions of scale, performance, and global delivery, while others revolve around API shape, data flow, and how product features are supported cleanly over time.

The prompt is deliberately broad, because that is how real problems tend to arrive. Your job is to give the conversation structure by surfacing the right requirements, narrowing the scope thoughtfully, and guiding the design toward something that balances user impact with technical reality. What matters most is not how polished the final diagram looks, but how comfortably you navigate between what the product needs and what the system can support, since that constant translation is a core part of how Meta engineers actually build.

If you are in a System Design interview, you can almost feel where the interviewer wants to spend time. They care about scalability, performance, efficiency, availability, and failure, because someone on that team is going to wake up to alerts when traffic spikes or a region flakes out. So the conversation drifts toward load patterns, guarantees, bottlenecks, and what breaks first when things get weird, which they always do at Meta’s scale.

Now shift chairs and imagine the same system from a Product Architectural Design angle. Suddenly the interesting questions are about API design, usability, utility, and product evolution. How do clients talk to this thing? What happens when product wants to change behavior without breaking older versions? How do you add features without turning the API into a museum of bad decisions? Same system, very different pressure points.

What Meta is listening for, in both cases, is whether you can move between those viewpoints without getting stuck in one lane. The strongest candidates make it obvious they understand where decisions live, how tradeoffs change depending on context, and how to explain those choices clearly.

Performance is evaluated across four explicit focus areas, and these are the exact dimensions interviewers use when writing feedback, whether the interview is System Design or Product Architectural Design. Prepfully Coaches who have given us direct insight into he criteria have also made sure to mention that the emphasis may shift, but the signals do not.

• Problem navigation

This is about how you orient yourself before you start designing. Interviewers are looking at whether you can organize the problem space, identify constraints, and reduce ambiguity early. For example, in a System Design interview, simply pausing to ask something like “Are we optimizing more for latency or for reliability here?” shows good judgment, because it makes your tradeoffs explicit. In a Product Architectural Design interview, clarifying who the primary client is or how frequently the API is expected to change is often enough to signal that you are thinking in terms of real product constraints. If you ever feel tempted to jump straight into boxes and arrows, that is usually your cue to step back and frame the problem first.

• Solution design

This is about how you move from framing into a working solution that holds together once you start pulling on it. Interviewers care much less about whether you mention every possible component and far more about whether the parts you do design connect cleanly and reflect good judgment. For example, in a System Design interview, you might be asked to design a messaging or feed system, and a strong move is to explicitly say you will walk through one critical path first, like the write path or a high-traffic read path, before expanding.

In a Product Architectural Design interview, this often shows up when the question turns into designing an API or service that multiple clients depend on. Being clear about where the API boundary lives, what responsibilities sit on each side, and why you chose that boundary is a strong signal, especially if you tie it back to how the product is expected to evolve. For instance, calling out that a boundary makes it easier to add new features without breaking existing clients shows you are designing with change in mind, not just the current requirements.

A common moment to watch for in both versions of the interview is when the design starts to sprawl. Strong candidates notice this happening and deliberately narrow the scope, focusing on the most important interactions rather than trying to cover everything.

• Technical excellence

This is where depth matters, but selectively. Interviewers expect you to dive into technical details when it is useful, identify dependencies, and reason through tradeoffs, risks, and failure points. A good signal here can be as simple as saying “This introduces a single point of failure, so we’d want to address that later with replication or fallback,” even if you do not fully design it. In System Design, calling out how the system behaves under partial failure shows maturity. In Product Architectural Design, acknowledging how a data model choice might limit future features shows the same instinct. Depth matters here, but this is the part where you show you can spot the sharp edges without trying to rebuild the entire system on the whiteboard just to prove you know how.

• Technical communication

This runs through the entire interview, and Meta pays close attention to it because it is often the fastest way to tell how someone will really work on a team. Interviewers are listening for how you explain your reasoning and how you adapt when the conversation shifts. One of the strongest signals shows up when a new constraint is introduced and you respond by calmly adjusting the design, explaining what changes and why, instead of defending what you already drew. That moment is less about being right and more about being easy to reason with.

Interviewers like hearing you say things like “that would change this part of the design” or “given that constraint, I would probably rethink this choice,” because it signals flexibility without flailing.

Ask Meta SWEs who already cleared this interview to see how the architectural bar differs across teams, levels, and product domains within Meta to prepare better.

System Design

In system design interviews, Meta expects you to understand how low-level constraints affect high-level goals. You should continuously talk through tradeoffs and design decisions while considering.

There are 8 things that you need to absolutely ensure you try and cover (unless of course your interviewer steers you away from them, which can happen if they want to get either a stronger signal on one of them; or already got a strong signal via something you talked through earlier)

• Testability and how you gain confidence in correctness
• Usability and how the system or product evolves quickly
• Extensibility as requirements change
• Security, including resilience against abuse or attacks
• Portability across environments
• Availability and failure recovery
• Scalability as usage grows
• Operational characteristics like diagnosing and debugging issues in production

What is worth internalizing during prep is that none of these show up as neat little boxes you check off one by one. They behave more like lenses you keep turning as the design takes shape.

Good system design practice is getting comfortable talking through how a seemingly small choice, like where state lives or how retries are handled, ripples into availability, operability, and developer velocity over time.

Product Architectural Design

In product architecture interviews, the focus is on building APIs or services that support end-user products at scale. You are expected to understand and weigh considerations such as storage data models, scalability, design patterns, data ownership, protocols, data formats, client-server design, and how to balance long-term flexibility against unnecessary complexity.

You are not expected to be an expert in every area, but you should know enough to prioritize decisions and recognize when expert input would be needed.

You should be thinking about:

• Where API boundaries live and why they belong there
• Who owns which data and what that ownership implies
• How storage data models support features without boxing you in
• What logic lives on the client versus the server, and why
• Protocol and communication choices and how chatty they get at scale
• Data formats and how versioning avoids awkward breakups
• How the system scales when usage grows faster than expected
• Which design patterns simplify things and which add friction
• How much flexibility you really need versus how much complexity you can afford
• What happens to your design when the product inevitably changes

Most importantly, you must remember that at Meta, product architecture work often sits at the seam between teams, and a good API is one that lets everyone move faster without stepping on each other’s toes.

Because interviewers can only assess what you surface in the moment, preparation pays off more here than almost anywhere else. Design interviews are meant to be interactive, so the real prep is learning how to think out loud, organize fuzzy problems, and move between big-picture ideas and specifics without losing your footing.

• Start with requirements

Your interviewer might ask: “How would you architect the front-end for a messaging system?” Obviously, this question is extremely vague so you practice by starting with some requirements:

• How would you architect the front-end for a messaging system? O
• How many users are we talking about?
• What should the experience be while you’re waiting for confirmation?
• How will you show error states?
• What are the latency requirements for sender–receiver message delivery?
• What kind of features are we going to need to support?
• What operations does this data store need to support?
• How do you push new messages to clients? Do you push at all, or rely on a pull-based model?
  

Through Prepfully, you can sit across from Meta engineers, from SWEs to Senior, Staff, and Engineering Managers, to understand the gears of Meta and prepare with a reference of what separates good from great at each level.

Browse for a Meta coach whose experience reflects where you want to be

• Design from the ground up
• Pick a system Meta or another large company already runs and mentally rewind it to day one. Now design it forward while keeping scale, bottlenecks, and failure modes in mind, and pay attention to how your choices would age over time. Designing an existing system from scratch forces you to confront tradeoffs that are usually hidden behind years of iteration. You have to decide where state lives, how components communicate, and which parts you want to keep simple early on.
• Improve upon a design
• Think about systems or products you have already built and give yourself permission to be a little opinionated about them. What would you keep if you had to build it again today, what would you change without hesitation, and where did things start to feel strained once scale or requirements shifted. Being able to articulate what you would change now, and why, shows that you understand second-order effects, which is exactly what design interviews are trying to surface. Being able to articulate what you would change now, and why, shows that you understand second-order effects, which is exactly what design interviews are trying to surface.
• Practice on paper
  Designing on paper is less about the paper and more about removing your usual escape hatches. When you cannot keep tweaking diagrams or adding just one more component, you are forced to decide what matters. That constraint surfaces how you prioritize, how you structure your thinking, and how quickly you converge on something workable. If you manage to design something out on paper, it will be way easier to transition to a competent design on a virtual whiteboard like Excalidraw.
• Think through tradeoffs
• Meta design discussions are rarely about finding the optimal answer and much more about choosing which costs you are willing to pay. Thinking through tradeoffs like latency versus consistency or flexibility versus operational complexity mirrors how decisions are made across products like Ads or Messaging, where optimizing one dimension almost always stresses another.
• When you are designing something that touches News Feed ranking, you can push for a heavier model that squeezes out incremental relevance gains, but you will feel that cost immediately in inference latency, GPU spend, and tail percentiles that affect billions of scrolls per day. Or you can constrain model complexity and accept a small relevance delta in exchange for predictable latency and infra efficiency at global scale.
• Also think about how in Messaging systems like Messenger or WhatsApp, consistency versus availability is an argument that won’t settle for the abbreviation of “CAP theorem”. If you bias toward stronger consistency for read receipts or message ordering guarantees, you may accept higher latency in certain regions or under network partitions. If you bias toward availability and local responsiveness, you need to reason carefully about reconciliation semantics and user trust when state converges later.
• In the interview, being explicit about these tradeoffs shows that you are thinking like someone who expects the system to live in production, not like someone solving a thought experiment.
• Be holistic and detailed
• Meta engineers routinely move between discussing high-level product behavior and very specific implementation details in the same conversation. Practicing this shift helps you avoid two common traps: staying so high-level that nothing feels real, or going so deep that the design loses coherence. Interviewers watch for this balance because it reflects how design reviews work across teams building Feed, Video, or Ads infrastructure.
• Reading, and then some more
• Reading engineering blogs from large companies gives you a window into how real systems evolve under pressure. The value is not in memorizing architectures, but in understanding why certain approaches failed or had to be revisited as usage grew. This prep helps you reason from lived examples when discussing things like scalability or failure modes, which tends to make your design decisions sound grounded and credible.

Interview prep:
Meta Software Engineer Interview Guide

Meta Software Engineer Initial Interview Guide

Meta Software Engineer Coding Interview Guide

Meta SWE Interview Question bank with answer reviewing tool

Meta SWE Mock Interview Coaches

Role specific prep:

Meta Senior vs Staff Engineer Interview Expectations

Understanding the AI assisted coding round

Engineering at Meta

Meta Code videos

Meta Opensource website

Engineering Leadership at Meta blog

Design interview resources:

System and Product design overview

GitHub Design Primer

Familiarise yourself with Excalidraw