PhD-level performance analysis and optimization for Google Cloud's control plane

This is an early-career PhD role on Google's Technical Infrastructure org, building unified debugging, profiling, and analytics tooling and exploring ML-based anomaly detection for Google Cloud control plane systems. The core of the job is VM performance analysis — finding bottlenecks, building performance models, designing benchmarks, and developing (and potentially patenting) novel optimization techniques, then publishing findings. Based in Warsaw, Poland.

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Likely format

Google standard loop: phone screen(s) with coding, followed by onsite rounds covering coding/algorithms, systems design or domain expertise (performance/OS), and Googleyness/leadership behavioral interviews

What this interview tests

Performance analysis and bottleneck identification in VMs/systems
Profiling and tracing tools and methodology
Operating systems and computer architecture fundamentals
Designing rigorous benchmarks to validate optimizations
Communicating research findings to technical and non-technical audiences

Common question themes

Describe a performance bottleneck you found in a system and how you diagnosed it

Walk through how you'd design a benchmark to evaluate a proposed optimization

Explain a novel optimization technique from your research and its tradeoffs

How would you approach anomaly detection in a large distributed control plane

Data structures/algorithms coding problem tied to systems performance

How candidates describe it

Real Software Engineer interview stories — retold from candidates' public write-ups, with sources.

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Interviewed January 2023 · Not specified

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