Google DeepMind Research Engineer Interview Questions

The questions to prepare for a Google DeepMind Research Engineer interview. Questions from real interview reports rank first. Updated daily.

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CodingStart here. 8 questions · ~69 min

Python Algorithm ImplementationEasy

Practice

Find the first occurrence of a target score in sorted Motion Recruitment Partners candidate results using binary search.

ArraysSearchingSorting

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Model Evaluation7 questions · ~60 min

Evaluate Model CalibrationHard

How to tell whether a model's predicted probabilities are well calibrated, and what the business impact is.

Log LossCalibrationAUC-ROC

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System Design8 questions · ~69 min

Online vs Batch Model ServingMedium

Compare batch and online serving for an ML ranking system, including freshness, latency, cost, and operational complexity.

Feature StoreRetrievalModel Serving

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NLP4 questions · ~34 min

Explain TF-IDF for Text FeaturesEasy

Explain TF-IDF and where it helps in text classification and search.

Text ClassificationTF-IDFTokenization

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Explain Word EmbeddingsEasy

Explain how word embeddings represent words as dense vectors and why they help NLP models capture meaning.

Language ModelsText ClassificationFeature Engineering

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Machine Learning8 questions · ~69 min

Feature Engineering for Supervised ModelsEasy

Explain feature engineering and why transforming raw inputs can materially improve supervised model performance.

Cross-ValidationFeature EngineeringSupervised Learning

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Statistics & Probability8 questions · ~69 min

Interpreting P Values in TestingEasy

Explain what a p-value means in hypothesis testing and how it relates to statistical significance.

Hypothesis TestingStatistical SignificanceP-Values

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