Google ML Engineer (L4/L5) Interview Experience, Hire?
Summary
I completed four interview rounds for a Google ML Engineer (L4/L5) role, including ML system design, Googliness, and two coding rounds, and ultimately received a hire/strong hire outcome.
Full Experience
🚀 Google ML Engineer (L4/L5) Interview Experience, Hire? 🤞
Total Rounds: 4 (2 Virtual + 2 Onsite)
I recently went through the Google ML loop and wanted to share a realistic breakdown — including mistakes, recoveries, and where things could go either way.
🔹 Round 1 — ML System Design (Virtual)
Problem: Design a Google Reviews–like system
We covered:
- NLP pipeline for reviews
- Deep dive into BERT
- Trade-offs: LLMs vs smaller models (latency, cost, quality)
- Unsupervised techniques for clustering / summarization
💡 Strong back-and-forth, deep discussion on modeling choices
Verdict: Hire (H) / Strong Hire (SH)
🔹 Round 2 — Googliness (Virtual)
Behavioral + situational:
- Ambiguous scenarios
- “Most complex project”
- Decision-making under uncertainty
⚠️ What went wrong:
- Answers weren’t as structured
- Missed depth in a few responses
📩 Recruiter feedback: Mixed
Verdict: Leaning Hire (LH)
🔹 Round 3 — Onsite Coding (Graphs + Heaps)
Problem:
- Movies with similarity relationships (transitive)
- Query:
- Highest-rated movie
- Tie → lexicographically smallest
- Follow-up → Top K movies
Approach:
- Build graph
- DFS to get connected component
- Initially used max heap
💥 Twist: Interviewer questioned heap usage → suggested a variable
👉 Then came follow-up: Top K movies
Recovery:
- Switched to min heap of size K
- Remove smallest when size exceeds K
💡 Initial approach scaled well for follow-up
⚠️ Minor hiccups:
- Forgot to exclude query movie initially
- Brief stumble on heap internals (recovered)
Verdict: Hire (H) / Strong Hire (SH)
🔹 Round 4 — Onsite Coding (DP / Combinatorics)
Problem:
Count subsets where: sum(subset) > total_sum / 2
Example:
[1,3,4], total = 8 → threshold = 4
Valid → [1,4], [3,4], [1,3,4] → answer = 3
Approach:
- Started with recursion (pick / not pick)
- ❌ Bug in base case → overcounting
- Switched to DP
💡 Key idea: Once sum > threshold → all remaining subsets are valid
⚠️ Still had a counting mistake, but discussion was decent
Verdict: Leaning Hire (LH)
📊 Final Self-Evaluation
| Round | Rating |
|---|---|
| ML Design | H / SH |
| Googliness | LH |
| Coding 1 | H / SH |
| Coding 2 | LH |
🧠 Honest Take
Strengths:
- ML depth
- Problem solving
- Ability to adapt mid-interview
Weaknesses:
- Behavioral structure
- Edge-case precision under pressure
👉 Feels like a borderline but positive loop overall
🤔 Question
Given:
- 2 strong signals
- 2 leaning hire
👉 Does this typically convert to an offer at Google?
Would really appreciate honest feedback — I might be over/underestimating.
Interview Questions (2)
Movies Similarity Graph - Highest Rated Query and Top K
Given a set of movies with similarity relationships that are transitive, you are asked to:
- Find the highest‑rated movie within the connected component of a query movie. If multiple movies share the highest rating, return the lexicographically smallest title.
- Follow‑up: Return the top K movies (by rating, with the same tie‑break rule) from that component.
The problem requires building a graph of movies, performing a DFS/BFS to identify the component, and then selecting the appropriate movies using heap or other selection techniques.
Count Subsets with Sum Greater Than Half of Total
Given an array of positive integers, count the number of subsets whose sum is strictly greater than half of the total sum of the array.
Example: Array = [1, 3, 4] Total sum = 8 → threshold = 4 Valid subsets: [1, 4], [3, 4], [1, 3, 4] Answer = 3