Google Cloud and TPU AI Infrastructure System Design interview questions
Google Cloud TPU documentation gives a concrete accelerator-system boundary for design practice. Start from the workload and its topology requirements, then separate that design from any claim about Google’s interview prompts. Questions tagged to Google Cloud and TPU appear first, followed by additional practice from our AI Infrastructure System Design track. Company tags indicate preparation relevance, not confirmed interview appearances. Confirm the current interview format with your recruiter.
Design a training service around a topology
- Place the job as a unit. Specify the accelerator slice, dataset feed and checkpoint destination. Explain what happens when enough devices exist in total but not in a compatible placement.
- Recover consistent progress. Track a durable checkpoint and the input position needed to resume it. Compare waiting for a replacement with restarting on another allocation, including lost work and data access rather than only device count.
Sources: Google Cloud TPU architecture. Reviewed .
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AI Infrastructure System Design questions tagged to Google Cloud and TPU
Additional AI Infrastructure System Design practice
These questions cover the same topic and are not tagged to Google Cloud and TPU. Use them to practise applying the ideas in a different situation.
Concepts for AI Infrastructure System Design preparation
These concept explanations cover the ideas used in the questions. Check each card for its access level.
This collection includes practice questions such as "Design a multi-region inference deployment: capacity per region, routing, failover, and getting the weights everywhere.", "Design serving for a video generation model: diffusion steps, batching, memory, and a latency profile unlike an LLM's.", "Walk me through an inference platform for a hosted LLM. What are the pieces, and what does each one do?". Company tags describe relevance to preparation; they do not confirm that Google Cloud and TPU asked these questions. Check each question for answer access.
Start with a question at your level, use the linked concepts to understand the mechanism, then practise explaining your decision and its limits. Review the company guide's sources and confidence notes, and confirm the current role and interview format with your recruiter.
Yes, under Software Engineer titles: TPU Compiler (including a PhD early-careers track), TPU Compiler Development Infrastructure, Cloud TPU, ML Infrastructure for agent systems, and Cloud AI. Google does not use AI infra as a title family; infra work is assigned at team match.
The documented Google loop: a recruiter screen, one or two 45-minute coding phone screens, then four to five onsite rounds with two coding, one system design (mid-level and above), a Googleyness round and sometimes leadership, followed by a hiring committee and team match. A 2025 SWE III AI/ML debrief reported graph problems with real-world constraints. TPU or CUDA depth is a team-match signal, not a standard round.
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