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Google

Senior

Apply AI/ML to fault-tolerance and reliability across Google's global data center fleet

Senior software engineer role building AI/ML models that predict, detect, and mitigate hardware and software faults across Google's entire infrastructure fleet, including ML TPUs. Requires 8 years of software development experience with 5 years specifically in ML infrastructure/design, analyzing petabytes of telemetry to improve reliability at hyperscale.

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可能的面试形态

Google SWE loop: phone screen(s) covering coding and ML/infra domain knowledge, followed by onsite rounds spanning coding, ML system design, and Googleyness/leadership behavioral interviews.

这场面试考什么

  • ML model design for fault prediction/anomaly detection at fleet scale
  • Working with large-scale telemetry and operational data pipelines
  • ML infrastructure: model deployment, evaluation, fine-tuning, debugging
  • Cross-functional partnership with hardware designers and SREs
  • Technical leadership: setting direction, translating findings for executives

常见提问方向

Design an ML system to predict hardware faults from fleet telemetry data

How do you evaluate a model when failure events are rare/imbalanced

Describe a project applying ML to an infrastructure reliability problem

How do you productionize and monitor an ML model that others depend on operationally

Explain a complex technical reliability finding to a non-technical stakeholder

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