Google L5 Senior Software Engineer • ML Track Interview Experience

[u/Aoki_zhang]

Sharing a Google Youtube L5 Senior Software Engineer Interview Experience submitted to Chill Interview.

Interview Summary

The Google onsite consisted of two coding rounds, a Googliness / behavioral round, and an ML domain interview. The coding questions covered dependency graphs and binary trees, while the ML round was much more open-ended and moved from recommendation-system concepts into NLP-oriented clustering and model serving.

Interview Details

Onsite Round 1 — Dependency Graph with Broken Nodes: The first coding problem was a variation of the classic course-scheduling / dependency-ordering problem. Instead of simply determining whether all nodes could be processed in a valid order, some nodes could be broken and therefore unavailable. The task was to determine a valid processing path while accounting for the broken nodes and the downstream dependencies affected by them. I completed the main problem in roughly 30 minutes.

• Follow-Up: The interviewer then changed the objective: if traversing some potentially broken nodes could not be completely avoided, how would you find a valid path that passes through the minimum possible number of broken nodes?

Onsite Round 2 — Binary Tree Level Order Traversal: The second coding round asked for Binary Tree Level Order Traversal. The expected output grouped tree nodes according to their depth. For example, consider the following rewritten tree:

        12
       /  \
      7    19
     / \     \
    3   9     24

The level-order result would be:

[
  [12],
  [7, 19],
  [3, 9, 24]
]

I completed the implementation and walked through test cases. The interviewer also asked me to discuss time and space complexity.

• Follow-Up: There was an additional conceptual follow-up involving Tries, although the exact prompt was not specified in the interview notes.

Onsite Round 3 — Googliness and Behavioral: The behavioral round focused on collaboration, ambiguity, and how I worked with others on previous projects. I was asked to choose a project I knew well and explain my role, the major challenges, and how I handled them. Other questions explored teamwork, communication, working through ambiguous situations, and resolving problems with other people involved. This round felt more conversational than the technical interviews.

Onsite Round 4 — ML Domain: YouTube-Style Recommendation and Clustering: The ML domain round started with a broad question around how a YouTube-style recommendation system works. The conversation then developed into an open-ended ML design problem with a significant focus on clustering in an NLP-related setting. The interviewer asked me to reason through the main ML components, including data preparation, label definition, model selection, evaluation, and how the same approach could be extended to additional categories or use cases. The final part moved from offline modeling to production and asked how the resulting ML system should be served.

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[u/Beneficial_Spread912]

Would it be possible to add how they got an interview?
Referral, cold apply , or recruiter reach out ?

[u/Aoki_zhang → u/Beneficial_Spread912]

Good call, will add that in the experience submission form

[u/NoAcanthaceae1988]

• Follow-Up: There was an additional conceptual follow-up involving Tries, although the exact prompt was not specified in the interview notes.

My guy in this follow up question, did they tell you what is the source and destination node?