Just finished onsite at Meta for E6 ML SWE today

Just finished onsite at Meta for E6 ML SWE today

Just Finished Onsite at Meta for E6 ML SWE Today

Today marked an important milestone in my career as I completed my onsite interview for the E6 Machine Learning Software Engineer position at Meta. Here’s a breakdown of my experience, including the coding challenges, system design discussions, and what I learned throughout the process.

Interview Rounds Overview

Round 1: Coding Challenges

The first round consisted of two coding questions that I found to be quite manageable, reminiscent of LeetCode-style problems.

  1. Breadth-First Search (BFS) Problem: This was a medium-level question that required a good grasp of BFS principles but was intuitive to solve.
  2. Linked List Palindrome: Another medium-level question, it required checking whether a linked list is a palindrome. No complex tricks were involved, making it straightforward.

Both questions felt accessible, and there were no follow-up questions, which allowed me to focus on solving them efficiently.

Round 2: More Coding Challenges

The second round had two distinct problems:

  1. Directory Manipulation: This was an easy question but required careful handling of edge cases, which I initially overlooked. The interviewer was flexible regarding the implementation, so I opted for a stack-based approach for easier management of right pops.

  2. Topological Sort: A foundational algorithm question, it was straightforward for anyone familiar with graph theory. I managed to solve it, but unfortunately, the interviewer ran out of time to review my solution in detail.

System Design Round

The system design segment comprised two parts focused on machine learning applications, both involving video-based scenarios. While these were relatively easy for me, I found the coding aspects to be more challenging.

Challenges Faced

In the first interview, I encountered significant frustration. The interviewer did not provide clear directives, which left me feeling uncertain about expectations. At the end, she mentioned that my data explanations were weak, but I felt the lack of explicit guidance contributed to this miscommunication. This experience taught me the importance of asking clarifying questions when unsure.

Conversely, my second system design interview flowed smoothly. The interviewer was highly engaging, nodding in agreement and expressing enthusiasm for my responses, which boosted my confidence.

Behavioral Interview

The behavioral segment was straightforward, though the interviewer was less interactive compared to the system design round. I felt comfortable discussing my experiences and motivations.

Overall Impression and Reflections

I believe I performed well in three out of five rounds: one coding, one system design, and one behavioral. However, I am apprehensive about the other two rounds, particularly since I presented a solution that was more effective than the interviewer’s thought process regarding recommendation rankings. Unfortunately, I didn’t articulate my reasoning well enough or challenge her assumptions, which is a lesson I will take with me.

I remain hopeful for positive feedback, especially given the mixed nature of my performance. I plan to share updates once I hear back.

Community Insights

Interestingly, I’ve heard from peers that Meta is currently grappling with interview staffing issues. One colleague mentioned that new hires are being quickly inundated with interview requests. This raises concerns about the quality of hires and the potential for high turnover rates in the near future.

Final Thoughts

As I reflect on this experience, I recognize the necessity of clear communication in interviews, both from the interviewer and the candidate. It’s crucial to ensure that expectations are aligned.

If anyone has further questions about the system design round or the overall interview experience, feel free to DM me. I’m happy to share more insights!

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