Meta SWE Interviews in 2026: AI Coding, High-Frequency Questions, and What Seems to Be Changing

I’ve been going through recent Meta interview reports from Glassdoor, Blind, LeetCode Discuss, and other public sources.

Not my personal interview experience — just a summary of patterns I’ve been seeing across 2026 reports.

The biggest change is obviously the AI-enabled coding round.

Instead of the traditional “here’s a LeetCode problem, start coding” format, some candidates have reported a CoderPad environment with an editor, file browser, and access to AI models.

The interesting part is that this round doesn’t seem to be about who can generate code the fastest.

It’s much more about whether you can:

• give the AI useful instructions
• understand the code it generates
• catch incorrect assumptions
• write tests instead of blindly trusting the output
• optimize the solution when requirements change

In other words, using AI is allowed, but blindly copying AI-generated code is probably the worst possible strategy.

Traditional coding rounds are still very much alive though.

The usual pattern still seems to be roughly two problems in ~35–40 minutes, so speed matters a lot. A solution you can finish comfortably in 50 minutes during practice may be very different under interview pressure.

A few coding topics also seem to show up repeatedly in recent reports:

• Binary Tree Vertical Order Traversal
• Lowest Common Ancestor
• Simplify Path
• Range Sum of BST
• Two Sum / stock-style questions as warmups
• stack / expression parsing
• streaming-style problems

One trend I find more interesting than the exact question list:

Meta seems to be using more variations of familiar problems rather than simply repeating the exact LeetCode prompt.

So memorizing the solution to a tagged problem is becoming less useful than understanding why the solution works and being able to adapt it quickly.

For system design, the questions still tend to be closely related to large consumer products:

• News Feed
• Messenger
• Instagram Stories
• notification systems
• recommendation / friend suggestion systems

And behavioral is something I definitely wouldn’t leave until the last minute.

Recent candidate reports continue to describe pretty deep questioning around impact, conflict, ownership, and project decisions, especially at senior levels.

If I were preparing for Meta right now, I’d probably focus on three things:

1. Know the common coding patterns extremely well

Not just the final solution. Be able to recognize the pattern and adapt it under time pressure.

2. Practice with a timer

Two mediums individually may feel easy. Two mediums plus explanation, debugging, and follow-ups in ~40 minutes is a different problem.

3. Practice coding with AI as a reviewer, not as an answer generator

Have AI generate something, then force yourself to review it, find edge cases, test it, and explain every important decision.

The interesting question is whether this becomes the new normal across big tech.

If engineers are increasingly expected to work with AI every day, testing whether someone can supervise AI-generated code may eventually be more useful than testing how fast they can type out a memorized algorithm.