Meta ML Engineer Interview – E4 (2022)

Position: Machine Learning Engineer (MLE)
Level: E4
Location: Canada
Interview Type: Onsite
Date: February 2022
Result: Rejected
Years of Experience: 2+
Background: CV-focused MLE

This post shares a detailed Meta E4 Machine Learning Engineer (MLE) interview experience. The candidate highlights what to expect across different rounds, what went well, and where things could have been better. While Meta’s MLE interviews resemble SWE interviews in many ways, the ML Design round adds a significant difference.

Interview Overview

The process started with a recruiter reaching out via LinkedIn. After a phone screen in January 2022, the onsite rounds were scheduled for February.

Phone Screen

• Question 1: Medium array problem

• Question 2: Medium tree problem (with follow-ups)
  
  

Both questions were solvable and could be found among LeetCode’s Top 100 problems. The candidate completed both just in time but didn’t have time for follow-up discussions.

Takeaway: Post-screen, the candidate focused on practicing how to communicate clearly while coding.

Onsite Interview Rounds

Coding Round 1

• Medium-level 2D array/matrix question, a variant of a common LeetCode pattern

• Medium tree problem from LeetCode Top 100
  
  

The candidate solved both questions. Initial nervousness affected performance on the first question, but the interviewer helped them relax. They had about 5 minutes left for Q&A.

Self-evaluation: Lean hire or hire

Coding Round 2

• Medium math-based question

• Easy tree problem
  
  

Both were familiar from LeetCode’s Top 50. The candidate solved both efficiently and had time left over.

Self-evaluation: Hire or strong hire

SWE System Design

The question was based on a common system found in Grokking and YouTube tutorials. The candidate followed a typical design approach and engaged in meaningful deep dives into some components.

While some struggle was noted due to limited real-world system design experience, the interviewer was collaborative, making the discussion feel like a team brainstorm.

Self-evaluation: Lean hire

ML Design Round

This was the most challenging round. The candidate was asked to design a recommendation or ranking system and discussed the flow from data collection to model deployment.

However, coming from a computer vision background, they lacked depth in ranking systems. The discussion remained broad rather than deep, and the interviewer seemed disengaged.

Self-evaluation: No hire
Key learning: Candidates with CV backgrounds should invest time preparing for recsys-style ML design rounds.

Behavioral Round

This round followed a structured format similar to Amazon’s leadership principles. The candidate was well-prepared and handled the questions confidently.

Self-evaluation: Hire or lean hire

Final Thoughts

This Meta E4 MLE interview loop involved six rounds: a phone screen, two coding rounds, one SWE system design round, one ML design round, and a behavioral round.

Key takeaways include:

• Meta MLE interviews are very similar to SWE interviews, with one additional ML Design round.

• Prepare for recommender system and ranking design even if your background is in CV or NLP.

• Practice thinking out loud and explaining your approach during coding interviews.

• Behavioral interviews are manageable with standard preparation methods.
  
  

Despite the final rejection, this candidate’s reflection is incredibly helpful for other ML Engineers targeting Meta’s E4 level.