Senior SWE, Google DeepMind — build and evaluate agentic Gemini features

A Google DeepMind role building and improving agentic AI features inside Google products on top of the Gemini models, plus the evaluation infrastructure to measure agent quality. You'll triage production issues, drive code/design review culture, and set short-term technical direction for a small team. Despite the generic 'Software Engineer' leveling label, the JD requires a PhD plus 1 year of relevant experience and heavy Go/Python fluency.

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What this interview tests

Agentic AI feature development on Gemini models
Quantitative evaluation design for agent performance
Production triage and debugging of agent/product issues
Go and Python for ML evaluation tooling
Algorithm/data-structure choices for system scalability
Software architecture for performance and reliability

Common question themes

Design an evaluation framework for an LLM agent's task success rate

Debug a production issue in an agentic feature — walk through triage

Choose data structures/algorithms for a system that must scale

Go vs Python tradeoffs for ML tooling vs production services

Drive a short-term technical roadmap disagreement in code/design review

How candidates describe it

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