High-Score Interview Experience: Google ML SWE (PhD) Loop — What the Tough Follow-ups Really Test

A concise write-up from a high-scoring candidate (non-CS background) who completed Google’s ML SWE PhD loop (4 rounds). This summary highlights what each round focused on, the key follow-ups asked, and practical takeaways for preparing effectively.

Quick overview

• Interview type: Google ML SWE (PhD) loop
• Rounds: 4 (ML fundamentals, Behavioral, Coding #1, Coding #2)
• Candidate background: non-CS
• Common theme: solve the core quickly, then expect optimizations and harder variants

ML fundamentals (round content)

Topics covered:

• Logistic regression
• Naive Bayes
• Transformers (architecture/intuition)
• Evaluation metrics (precision, recall, F1, AUC, etc.)
• Ensemble methods (bagging vs boosting)

What they tested:

• Depth of conceptual understanding (not just definitions)
• Knowing when to use each model and their trade-offs
• Interpreting metrics in context (class imbalance, business trade-offs)

Prep tips:

• Be ready to explain assumptions, limitations, and complexity trade-offs.
• Review example scenarios where one metric is preferred over another.

Behavioral (round content)

Focus areas:

• Impact of your dissertation (or research) — articulating novelty, impact, and metrics of success
• Handling disagreement with a supervisor — communication, data-driven persuasion, escalation strategy

Prep tips:

• Use STAR format: Situation, Task, Action, Result. Quantify impact where possible.
• Prepare at least one concrete example of a disagreement and how you reached a constructive outcome.

Coding round 1 — Shortest path with blocked nodes

Problem sketch:

• Find shortest path in a grid/graph when some nodes are blocked.
• Core solution: BFS for unweighted shortest path.

Follow-ups / harder variants asked:

1. Space optimization — reduce memory usage (e.g., in-place marking, using bitsets, compressing visited structure).
2. Variant with higher traversal cost — edges/nodes with weights. This pushes toward Dijkstra or A* and reasoning about heuristics if applicable.

Key expectations:

• First, deliver a correct BFS implementation quickly.
• Then explain and implement optimizations while keeping correctness.
• Finally, adapt to weighted traversal by discussing algorithmic changes and complexity.

Prep tips:

• Practice BFS/DFS and common space optimizations.
• Be ready to justify switching to Dijkstra and to discuss admissible heuristics if A* comes up.

Coding round 2 — Top-k / list-avoidance constraint

Problem sketch:

• Given listA (top-k items) and listB, remove items from listB so the top-k selection doesn’t overlap with listA.
• Extension: multiple lists with constraint “avoid items that appear in the last d lists.”

Follow-ups / harder variants asked:

• Generalize to multiple lists, enforcing an "avoid last d lists" constraint.
• Consider performance when lists are large or when k is large relative to list sizes.

Key expectations:

• Provide a clear core solution (hash sets, priority queues) quickly.
• Then discuss scalability, edge cases, and trade-offs for streaming or memory-limited scenarios.

Prep tips:

• Be comfortable with sets, heaps, frequency maps, and sliding-window style constraints.
• Think about online/streaming versions if inputs are too large to store.

Key takeaways

• Solve the core problem quickly and correctly — interviewers expect a working baseline fast.
• Expect iterative follow-ups: time/space optimizations and problem generalizations.
• Explain trade-offs and clearly state complexity (time & space) after each improvement.
• For ML rounds, focus on intuition, assumptions, and when a model is appropriate.
• For behavioral, be concrete: quantify impact and show collaborative problem-solving.

Practical checklist to prepare

• Brush up: BFS/DFS, Dijkstra, heaps, hash sets, priority queues.
• Practice optimizing memory and time — in-place, bitsets, streaming.
• Review ML fundamentals: logistic regression, Naive Bayes, transformers, evaluation metrics, bagging vs boosting.
• Prepare 3–4 behavioral stories with clear metrics and outcomes.
• During interviews: communicate assumptions, test edge cases, and iterate from core solution to optimized variants.

Good luck — focus on getting a correct baseline quickly, then use the extra time to demonstrate depth by optimizing and generalizing your solution.