Real, anonymous reports from people who interviewed for Machine Learning Engineer at Meta, newest first and distilled into what to expect across the loop.
I kept expecting it to be more complicated, but the process I experienced was pretty straightforward. First came a screen round with a behavioral segment plus two LeetCode questions—one medium and one hard—each only about 15 to 17 minutes. If I cleared that, the loop that followed was essentially a smaller set of interviews: four to six rounds total, with two design rounds, two coding rounds, and one behavioral.
After applying online, I got a recruiter screen within about two weeks. That call was mostly background and role fit, plus a bit of direction on what they were looking for. A short time later I did a technical phone screen where I coded through a LeetCode-style problem in the medium to hard range, with a focus on arrays and graphs.
The interview process begins with a recruiter screen that typically covers background, role fit, and logistics. Candidates noted varying levels of supportiveness and clarity during this initial interaction, which can set the tone for the rest of the process.
Candidates undergo a technical screen involving two LeetCode-style coding problems, usually of medium to hard difficulty, often with a focus on arrays and graphs. The time pressure during this round is significant, and candidates are expected to communicate their thought processes clearly.
The onsite typically includes two coding rounds that assess algorithmic skills and problem-solving under time constraints. Candidates should prepare for a mix of easy to medium-level problems, with an emphasis on clean code and edge case consideration.
An ML system design round is a key component of the interview, requiring candidates to structure an end-to-end pipeline for a real-world problem, such as a recommendation system. Depth of understanding and the ability to discuss trade-offs are crucial in this round.
Behavioral interviews focus on past experiences, teamwork, and conflict resolution, often using the STAR method. Candidates should be prepared to discuss leadership principles and how they handle ambiguity and challenges.
Candidates generally describe the interview process as structured and professional, though some noted issues with pacing and communication clarity. The timeline from application to final decision can take several weeks, and feedback may not always align with interview performance.