Interview questions
Google DeepMind AI engineer interview questions (2026)
6 real interview questions reported by engineers who interviewed at Google DeepMind, spanning AI Engineering, ML Engineering. Every question is scored against a golden answer on the things Google DeepMind actually grades — architecture, token efficiency, security and correctness — not just whether your code runs.
Google DeepMind AI Engineering questions
- LLM Agent Tool Calls Exceed Context Limit Token Budget
Your LLM agent is hitting token limits on tool calls. The tool calling API has a separate context limit and the agent is exceeding it. Find out why tool calls are consuming so many
- Audio Denoising Pipeline With Spectral And Temporal Filtering
Design a denoising system for sounds.
- Measuring and Mitigating Hallucination in Production LLMs at Scale
How would you measure and mitigate hallucination in a production LLM serving 1M daily queries? What's your eval framework and what does your monitoring stack look like?
- Design a multi-tenant vector database for 10,000 enterprise customers
You are designing the vector database layer for an enterprise RAG platform serving 10,000 companies. Each company has its own document corpus. Requirements: strict data isolation —
Google DeepMind ML Engineering questions
- Multi-tenant vector database with strict isolation and sub-100ms query latency
Design a multi-tenant vector database serving 10,000 enterprise customers. Each tenant requires strict data isolation. Support up to 10M vectors per tenant at sub-100ms p95 query l
- ML Experiment Tracking System with Metrics Analysis
Design an ML experiment tracking and analysis platform.
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