Mid
Build AI-native agentic systems on Google's Core Acceleration Platform team in Singapore
Prep for a Google Software Engineer role on the Acceleration Platform team (part of Google's Core org, based in Singapore), building agentic AI systems, RAG pipelines, and eval frameworks meant to eliminate developer toil at enterprise scale. Despite the ambitious agentic-AI scope, the JD's minimum bar is 2 years of Python/C++ development plus 1 year in a core ML domain — so this mock is calibrated as a mid-level (not staff) role with strong LLM/agent-systems flavor.
Step into this interview
Free · a live voice mock calibrated to this exact role
Likely format
Google's standard SWE process is publicly known: phone screen(s) with coding, followed by an onsite loop of coding, a Googleyness/leadership behavioral round, and a role-relevant ML/systems depth round.
What this interview tests
- Python or C++ software engineering fundamentals
- Agentic AI system design (planning, reasoning, multi-step execution)
- Retrieval-Augmented Generation (RAG) and prompt engineering
- Building evaluation pipelines for AI/agent behavior
- Debugging non-deterministic model behavior
- AI safety / secure-by-default agent design
Common question themes
Design an AI agent that plans and executes a multi-step engineering task
How would you build an eval pipeline to measure agent performance
Debug an AI agent that behaves inconsistently across runs
Walk through a RAG system you designed — retrieval quality and grounding
How do you keep an autonomous agent secure-by-default
General coding fundamentals in Python or C++
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