Leading technology company specializing in search, cloud, and AI.
4 Rounds
~21 Days
Very Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you discovered a significant bias or data leakage in your ML model right before deployment. How did you handle it, and how did you communicate the delay to stakeholders?
#Googleyness
#Communication
#Model Debugging
#Ethics
Machine Learning Engineer
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Behavioral
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medium
Describe a situation where you had to push back on a Product Manager who wanted to launch an ML feature that achieved high accuracy but failed to meet the required P99 latency SLA.
#Conflict Resolution
#Prioritization
#Cross-functional Collaboration
Machine Learning Engineer
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Coding
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medium
Given a 2D grid representing a cluster of TPU v5e pods where '1' is an active pod and '0' is inactive, write an algorithm to find the maximum area of connected active pods. Pods are connected horizontally or vertically.
#Graph Theory
#Depth-First Search
#Breadth-First Search
#Matrix
Machine Learning Engineer
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Coding
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hard
Given an array of integers representing the execution times of ML training jobs and an integer K representing the number of available GPUs, partition the jobs to minimize the maximum execution time on any single GPU. Jobs must be scheduled in contiguous subarrays.
#Binary Search
#Greedy Algorithms
#Dynamic Programming
Machine Learning Engineer
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Coding
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medium
Implement a custom sparse matrix-vector multiplication (SpMV) algorithm. Assume the sparse matrix is provided in Compressed Sparse Row (CSR) format.
#Linear Algebra
#Data Structures
#Performance Optimization
Machine Learning Engineer
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Coding
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hard
Write a function to sample a random node from a massive, distributed graph where you only have access to an API `get_neighbors(node_id)`. You do not know the total number of nodes.
#Randomized Algorithms
#Graph Theory
#Reservoir Sampling
#Markov Chains
Machine Learning Engineer
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System Design
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hard
Design the recommendation system for YouTube Shorts. Specifically, how would you handle the cold-start problem for new creators and optimize for real-time engagement metrics like watch time and swipe-aways?
#Recommendation Systems
#Two-Tower Models
#Cold Start
#Real-time Streaming
Machine Learning Engineer
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System Design
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hard
Design a system to predict Ad Click-Through Rate (CTR) for Google Search. How do you handle categorical features with massive cardinality, and how do you update the model with fresh data throughout the day?
#CTR Prediction
#Feature Engineering
#Continuous Training
#Embeddings
Machine Learning Engineer
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System Design
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hard
Design an autocomplete/typeahead system for Google Docs using a neural language model. The system must run within strict latency constraints (<50ms). How do you optimize the model and serving infrastructure?
#Low Latency Serving
#Model Quantization
#Sequence-to-Sequence
#Edge ML
Machine Learning Engineer
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System Design
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medium
Design a system to detect policy-violating images (e.g., hate speech, extreme violence) uploaded to Google Drive. The system must process millions of images per minute with extreme precision to avoid false positives on user data.
#Computer Vision
#High Throughput
#Active Learning
#Anomaly Detection
Machine Learning Engineer
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Technical
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hard
Explain how you would implement KV-caching in a Transformer model during autoregressive inference. What are the memory bottlenecks, and how do techniques like PagedAttention address them?
#Transformers
#LLM Inference
#Memory Optimization
#Attention Mechanisms
Machine Learning Engineer
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Technical
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hard
How does Distributed Data Parallel (DDP) differ from Fully Sharded Data Parallel (FSDP) or ZeRO optimization when training large language models? When would you choose one over the other?
#Distributed Training
#Model Parallelism
#Data Parallelism
#Memory Management
Machine Learning Engineer
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Technical
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medium
You are training a multimodal model (text and image) using a contrastive loss similar to CLIP. You notice the text loss converges much faster than the image loss, leading to poor alignment. How do you diagnose and fix this?
#Multimodal ML
#Loss Optimization
#Contrastive Learning
#Debugging
Machine Learning Engineer
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Technical
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medium
Explain the mathematical difference between Layer Normalization and Batch Normalization. Why is Layer Normalization almost exclusively used in Transformer architectures instead of Batch Normalization?
#Normalization
#Transformers
#Mathematics
Machine Learning Engineer
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Technical
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medium
How would you evaluate the quality of a Retrieval-Augmented Generation (RAG) system built for Google Cloud enterprise search? What specific metrics would you use for the retrieval component vs. the generation component?
#RAG
#LLM Evaluation
#Information Retrieval
#Metrics
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.