New grad
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.
Step into this interview
Free · a live voice mock calibrated to this exact role
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
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