Senior
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
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