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