INTERVIEW GUIDE

Meta Machine Learning Engineer Interview: Questions & Process

Meta's Machine Learning Engineer loop is the SWE loop plus ML depth. You'll still face fast, LeetCode-style coding rounds, but you'll also get an ML system design round (think recommendation and ranking) and ML domain questions on top of the behavioral 'Jedi' round. Strong coding is the floor; what separates candidates is designing real ML systems and reasoning about models end to end.

The interview process

1. Recruiter screen — ~30 min call

Tests: background, ML experience, team/level fit, and timeline

2. Technical phone screen — 45 min on CoderPad

Tests: two medium data-structures/algorithms problems coded live — same coding bar as the SWE loop

3. Coding onsite — 45 min, two problems

Tests: arrays, strings, graphs, and trees with clean code and complexity analysis under time

4. ML system design — 45-60 min

Tests: designing an end-to-end ML system — features, model choice, training/serving, metrics, and feedback loops (e.g., a recommendation or ranking system)

5. ML domain + behavioral (Jedi) — 45 min each

Tests: ML fundamentals (bias/variance, regularization, evaluation) and impact, drive, and collaboration stories

Questions you're likely to get

Technical

Merge a set of overlapping intervals and return the consolidated list.
Count the number of islands in a 2D grid using BFS or DFS.
Return an array of products of all other elements without using division.
Find the kth largest element in an unsorted array efficiently.

Role-specific

Design the ML system behind 'recommended Reels' for a new user with little history.
How would you design a ranking model for the news feed, and what features would you use?
Your model's offline AUC improved but online engagement dropped. What's going on?
How do you handle severe class imbalance in a fraud or spam classifier?
Explain bias-variance tradeoff and how you'd diagnose overfitting in production.
How would you choose an evaluation metric for a recommendation system?

Behavioral

Tell me about an ML project you owned end to end and its measurable impact.
Describe a time you moved fast and shipped a model despite uncertainty.
Tell me about a time you disagreed with a teammate on modeling approach.
Why Meta, and which ML problem here excites you most?

Practice these problems live

Relevant LeetCode problems for the Meta Machine Learning Engineer loop. Start a live, AI-run coding interview on any of them — or open the problem on LeetCode.

▶ Merge Intervals· Medium · LeetCode #56

Merge Intervals is a Meta coding staple that still appears in MLE loops.

▶ Number of Islands· Medium · LeetCode #200

Number of Islands tests the grid BFS/DFS Meta asks across SWE and MLE.

▶ Product of Array Except Self· Medium · LeetCode #238

Product of Array Except Self is a favored no-division array problem.

▶ Kth Largest Element in an Array· Medium · LeetCode #215

Kth Largest checks heap/quickselect instincts under Meta's time pressure.

▶ Top K Frequent Elements· Medium · LeetCode #347

Top K Frequent Elements blends hashing and heaps, useful for data-heavy MLE work.

▶ LRU Cache· Medium · LeetCode #146

LRU Cache tests design fluency that maps to caching features and embeddings.

How to answer (worked examples)

Design the ML system behind 'recommended Reels' for a new user.

Frame it end to end and name the cold-start problem up front. Clarify the objective and metric (watch time, retention, not just clicks). Walk through candidate generation, ranking, features (content, context, lightweight user signals), and how you bootstrap a new user with popularity and exploration. Cover training data, online/offline metrics, and the feedback loop, plus failure modes like clickbait or feedback amplification. Meta wants a coherent system, not a list of model names.

Your model's offline AUC improved but online engagement dropped.

Reason about the gap between offline and online. Candidates: training/serving skew, label leakage, a metric that doesn't reflect user value, distribution shift, or feedback-loop effects. Propose how you'd isolate each (logging audits, A/B holdouts, slicing by segment) and emphasize that the online metric is the truth. Showing you trust experiments over offline numbers is exactly the maturity Meta screens for.

What Meta looks for

SWE-level coding: two mediums per round, clean and fast
End-to-end ML system design — data, features, training, serving, and metrics
Sound ML fundamentals and honest reasoning about model behavior
Judgment about online vs offline metrics and real user impact
Quantified impact and ownership in behavioral stories
Red flag: strong theory but no instinct for production tradeoffs or experimentation

FAQ

Do MLE candidates still do LeetCode coding rounds?

Yes. Meta's MLE loop keeps the same coding bar as SWE — expect two mediums per 45-minute round. Don't let ML prep crowd out algorithm practice.

What does the ML system design round cover?

Designing a real ML system end to end — usually recommendation or ranking. They want candidate generation, ranking, features, training/serving, metrics, and feedback loops, plus failure modes.

How deep do the ML domain questions go?

Solid fundamentals: bias-variance, regularization, evaluation metrics, handling imbalance, and overfitting. New grads aren't expected to know everything, but you should reason clearly about models.

Is research experience required?

Not for the engineering MLE track. Applied ML, strong coding, and systems thinking matter more than publications — that's more the Research Scientist path.

How long is the process?

Typically 4-6 weeks from screen to offer, depending on team matching and scheduling.

Cover coding and ML design in one breath

Meta's MLE loop demands fast coding and a clean end-to-end ML design out loud. Rehearse the LeetCode rounds, an ML system walkthrough, and the Jedi behavioral with OfferLoop's realtime voice coach.

Practice this interview out loud →

All Meta interviews →Meta Software Engineer interview →Meta Data Analyst interview →Meta Data Scientist interview →Meta Data Engineer interview →

OfferLoop is an independent interview-practice tool and is not affiliated with, endorsed by, or sponsored by Meta. All company names and trademarks are the property of their respective owners.

Interview formats vary by team, level and year, and this guide is compiled from general knowledge of publicly discussed hiring processes — treat it as preparation material, not an official description of Meta's current process.