Optimize Google's GPU software stack from ML compiler cost models down to kernel-level performance

Join Google's Core ML organization to build optimizations for the latest generation of GPUs powering Google's ML infrastructure at massive scale. This role spans the full GPU software stack — from compiler cost model design to high-performance kernel tuning to cross-node model serving configuration.

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What this interview tests

Low-level GPU programming (CUDA/Triton/CUTLASS)
GPU performance engineering and profiling
GPU memory hierarchy and architecture
ML compiler optimization (OpenXLA/MLIR)
LLM deployment on accelerators

Common question themes

Walk through profiling and fixing a GPU kernel bottleneck

Memory-bound vs. compute-bound kernel diagnosis

How a compiler lowers/schedules ops for a GPU target

Tradeoffs in kernel tuning (occupancy, register pressure, tiling)

Cross-node serving considerations for large models

How candidates describe it

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

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Interviewed June 2020 · Bangalore, IN

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Google L4 Software Engineer Interview: Eight Rounds, No Offer

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Google L5 software engineer interview: phone screening, three onsite rounds, system design, and a late rejection

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Interviewed January 2023 · Not specified

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