🚀 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.
