The Interview

What Google is looking for

ML System Design Interview

ML System Design

01End-to-End ML Pipeline Architecture

02Training Infrastructure at Scale

03Model Serving & Inference Optimization

04Data Pipeline Design

Coding Interview

ML Engineering Coding

01Efficient Tensor Operations

02Distributed Training Patterns

03ML-Specific Data Structures

04Algorithmic Problem Solving

Behavioral Interview

01Googleyness & Leadership

02Technical Collaboration

03Ambiguity Navigation

04Impact-Driven Engineering

DeepMind + Brain engineering bar//Massive-scale ML infrastructure//Production AI system design

How It Works

Master Google AI Engineering Interviews

ML system design and coding rounds, scored on Google’s AI engineering evaluation dimensions. · Click to expand

1Your AI Engineer Goals

LLM System Design

Practitioner

Inference Optimization

Practitioner

2AI Engineer Case Prep

Design a distributed training architecture for a 1T-parameter MoE model on 4096 TPU v5e...

CASE INTERVIEWHard

Google

3Frameworks

Framework

Is the inference serving layer the bottleneck?

Is GPU memory pressure evicting the KV cache?

GPU memory utilization: 94%; KV cache eviction rate up 800% vs baseline

Fallback to paged KV cache adding +240ms per request at p99

Is dynamic batching creating queue depth spikes?

P99 queue wait time: 12ms → 380ms under 10× load (SLA: <50ms)

Max batch size capped at 8 — tuned for <200ms SLA at 1× load, no auto-scale policy

Is the RAG retrieval layer adding latency under load?

Is the vector store throughput saturated?

Vector index hitting 7.9K QPS (limit: 8K) — 12% of queries experiencing retry backoff

Embedding server latency: 12ms → 85ms under load (embedding model not horizontally scaled)

Is context assembly triggering expensive context-window switches?

k=10 chunks × 512 tok = 5,120 context tokens, forcing 4K→8K context switch on 68% of requests

8K context window increases inference time 1.4× due to quadratic attention cost

Is semantic caching failing to absorb repeated queries?

Is the semantic cache similarity threshold misconfigured?

Cache hit rate: 22% vs expected 40% for FAQ-heavy traffic pattern at 10× load

Cosine similarity threshold set to 0.97 — nearest neighbors at 0.91–0.95 not being served from cache

4Feedback

LLM System Design

Distinctive

Inference Optimization

Strong

Evaluation Design

Meeting Bar

ML Diagnostics

Strong

Repeat

Practice Library

Google exercises

Design the ML serving infrastructure for Google Translate to handle 100B+...

CASE INTERVIEWHard

AI / Infrastructure · ML System DesignGoogle

Google wants to fine-tune a foundation model for medical imaging classification. Design...

LEARNHard

AI / Infrastructure · Training PipelineGoogle

A deployed recommendation model’s latency increased from 20ms to 180ms after a model...

LEARNMedium

AI / Infrastructure · Production DebuggingGoogle

Prepare for your Google AI engineering interview

What Google is looking for

ML System Design

ML Engineering Coding

Behavioral Interview

Google exercises

Ready to ace your Google AI engineering interview?

Prepare for your Google AI engineering interview

What Google is looking for

ML System Design

ML Engineering Coding

Behavioral Interview

Master Google AI Engineering Interviews

Design a distributed training architecture for a 1T-parameter MoE model on 4096 TPU v5e...

Master Google AI Engineering Interviews

Design a distributed training architecture for a 1T-parameter MoE model on 4096 TPU v5e...

Google exercises

Design the ML serving infrastructure for Google Translate to handle 100B+...

Google wants to fine-tune a foundation model for medical imaging classification. Design...

A deployed recommendation model’s latency increased from 20ms to 180ms after a model...

Ready to ace your Google AI engineering interview?

Master Google AI Engineering Interviews

Design a distributed training architecture for a 1T-parameter MoE model on 4096 TPU v5e...

Master Google AI Engineering Interviews

Design a distributed training architecture for a 1T-parameter MoE model on 4096 TPU v5e...

Design the ML serving infrastructure for Google Translate to handle 100B+...

Google wants to fine-tune a foundation model for medical imaging classification. Design...

A deployed recommendation model’s latency increased from 20ms to 180ms after a model...