Google ML SWE Interview – L5 (2025)

Company: Google
Role: Senior Software Engineer – Machine Learning (L5)

Overview

An experienced ML Engineer recently interviewed with Google for the L5 role (Senior Software Engineer – ML). The interview process involved multiple rounds assessing coding proficiency and machine learning system design. Based on the initial feedback, Google requested two additional coding rounds to gather more signal. Here's a detailed breakdown of all rounds and the candidate’s feedback.

Interview Rounds

💻 Coding Round 1 – Hire

Problem: Implement a thread-safe Least Recently Used (LRU) cache that supports concurrent access.

• Focus was on data structures (HashMap + Doubly Linked List) and thread safety using locks.

• The candidate discussed lock granularity and performance trade-offs.
  
  

Evaluation: Clean implementation, good understanding of concurrency.
Feedback: Hire

💻 Coding Round 2 – Lean Hire

Problem: Given a matrix where each row is sorted and columns are not, find the k-th smallest element efficiently.

• The candidate used a min-heap with pointers to simulate merging sorted lists.

• Handled edge cases and explained space/time complexity.
  
  

Evaluation: Took some time to finalize the optimal approach.
Feedback: Lean Hire

💻 Coding Round 3 – Strong Hire

Problem: Design and implement an autocomplete system.

• Trie-based implementation with frequency count.

• Discussed memory optimizations, scalable query processing, and incremental learning.
  
  

Evaluation: Excellent communication, strong problem-solving, and performance considerations.
Feedback: Strong Hire

🧠 ML Design Round – Lean Hire

Prompt: Design a real-time recommendation engine for a short-form video app.

• Candidate proposed a two-tower model with user and item embeddings.

• Explained cold-start strategies, negative sampling, feature engineering, and online learning.

• Suggested use of FAISS for ANN search, and explained training loop and data pipelines.
  
  

Evaluation: Good understanding of modern ML systems. Some depth missing in scalability questions.
Feedback: Lean Hire

Additional Rounds (Re-Rounds)

🔁 Coding Re-Round 1 – Lean Hire

Problem: Implement a scheduler that executes tasks with dependencies (like a CI/CD system).

• Used topological sort and queue-based execution.

• Edge cases well-handled, but minor optimization missed.
  
  

Feedback: Lean Hire

🔁 Coding Re-Round 2 – Hire

Problem: You are given a stream of logs containing user activity (timestamped). Implement a system to return the number of active users in the last N minutes.

• Used deque for sliding window and hashmap for user tracking.

• Discussed memory efficiency and concurrent updates.
  
  

Feedback: Strong coding skills, understood production readiness.
Feedback: Hire

Final Verdict

Despite a mixed rating in a couple of rounds, the strong performance in key coding and ML rounds likely made a solid case. The additional re-rounds helped in strengthening the overall hiring signal.