Senior
Serving infrastructure for Gemini models at Google DeepMind
Join Google DeepMind's team building and scaling serving infrastructure for Gemini models in New York, including large-scale streaming and audio orchestration capabilities. This role requires production ML infrastructure experience — productionizing large language and multimodal models, not just training or research prototyping — plus C++ systems chops and the ability to drive technical direction for a team. Suited for senior engineers who've shipped ML systems at real production scale.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
可能的面试形态
Google's standard onsite loop typically includes coding, system design/ML infra design, and Googleyness/leadership behavioral rounds.
这场面试考什么
- Productionizing LLMs/multimodal models (serving, deployment, evaluation)
- C++ for performance-critical systems
- Algorithm/data-structure choices for scalability
- Root-cause analysis and production debugging of ML systems
- Software architecture for reliability and performance
- Driving technical direction and team roadmap
常见提问方向
Design a serving system for a large multimodal model with diverse client configurations
Describe debugging a production quality or latency regression in an ML system
How do you choose data structures/algorithms when scaling a system
Tell me about a technical direction or architecture decision you drove for your team
How would you build monitoring to catch model quality issues in production
Explain a large-scale streaming or real-time infrastructure challenge you've solved
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