Meta MLE Interview – E6 (2025)

This post details a comprehensive Meta E6 ML Engineer (MLE) interview experience, sharing real insight into what senior candidates can expect during the onsite loop. The candidate interviewed at Meta’s Menlo Park office for an E6 MLE position. With over 14 years of experience in machine learning and platform engineering, they were evaluated across multiple rounds covering algorithms, system design, ML system design, and leadership competencies.

Interview Process Overview

The entire interview loop was split over two consecutive days and included:

• Three rounds of coding and problem solving

• One round of system design

• One round of machine learning system design

• One behavioral and leadership round

• One calibration round (hybrid coding + behavioral)
  
  

Each round was conducted by senior engineers or staff-level interviewers.

Day 1 – Coding Rounds

Coding Round 1

• Q1: Design and implement an LRU cache

• Q2: Count the number of islands in a binary grid (1s and 0s)
  
  

✅ Both problems were solved optimally with minor clarifications on edge cases.

Coding Round 2

• Q1: Merge k sorted linked lists

• Q2: Build a trie to detect the most frequent phrase match in a given corpus
  
  

✅ This round focused on runtime/memory optimization and data structure design.

Coding Round 3

• Q1: Implement a freshness-guaranteed API wrapper

• Q2: Solve a recursive DP problem using memoization
  
  

✅ Interviewer emphasized robustness, testability, and production-readiness.

Day 2 – Design & Leadership Rounds

System Design

Problem: Design a personalized content-ranking system (Meta News Feed)

Key discussion points:

• Real-time and batch feature engineering

• Model experimentation framework

• Latency vs accuracy trade-offs

• Retrieval: ANN, vector similarity

• Scalability to billions of users
  
  

ML System Design

Problem: Design a system to detect and suppress policy-violating UGC (User-Generated Content)

Key topics covered:

• Multi-modal input (text + image + behavioral signals)

• Model architecture for fast and accurate predictions

• Online vs offline training trade-offs

• Human-in-the-loop feedback integration

• Adversarial example defense and drift monitoring
  
  

💡 This was the most in-depth and challenging round with production-level expectations.

Behavioral / Leadership Round

Covered Meta’s collaboration and execution bar:

• Leading cross-functional ML projects

• Driving alignment between research and product teams

• Handling incomplete data in decision-making

• Mentoring engineers and driving technical culture
  
  

✅ Candidate demonstrated strong leadership principles and problem-solving maturity.

Calibration Round

A hybrid round combining coding and behavioral evaluation.

Coding Question

• Q: Implement a data structure that supports insert, delete, and getRandom in constant time.
  
  

Behavioral Topics

• Navigating ambiguity in high-stakes ML projects

• Influencing product direction with limited signals

• Driving clarity across organizational layers
  
  

Final Thoughts

The E6 ML Engineer interview process at Meta is both deep and broad, demanding technical excellence and leadership capability. Success in this loop hinges on:

• Proficiency in scalable system and ML design

• Strong understanding of model lifecycle and monitoring

• Ability to communicate and lead across teams
  
  

Senior ML engineers should be prepared to balance architectural rigor with practical decision-making in production environments.