PhD engineer building end-to-end ML systems across Google's stack

This is a 2026-start, early-career PhD role open across multiple Google product areas (AI & Infrastructure, Cloud, YouTube, Search, Ads). The work spans the full ML stack — from low-level hardware acceleration and compiler optimizations up to model architecture and production APIs — plus optimizing production system performance (bottlenecks, memory inefficiencies, errors) and writing well-tested, reviewed code. Preferred experience includes deep learning frameworks (TensorFlow/JAX/PyTorch), model architectures (CNNs, NLP transformers, diffusion/vision transformers), and building a full AI application stack from data pipelines to user-facing APIs.

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Likely format

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

What this interview tests

End-to-end ML system design: data pipeline to model to production API
Deep learning frameworks and model architecture tradeoffs (CNNs, transformers, diffusion)
Diagnosing and fixing performance bottlenecks/memory inefficiencies in production ML systems
Research-to-production translation of ML/AI work at large scale
Coding fundamentals in Python/C/C++/Java/Go with well-tested, reviewed code

Common question themes

Walk through an ML project you took from research idea to a working, testable system

Why did you choose a particular model architecture (CNN/transformer/diffusion) for a given problem

Describe a performance bottleneck or memory inefficiency you diagnosed in a large-scale system

How would you design the data ingestion and API layer for an AI-powered application

Coding/algorithms question in one of Python, C, C++, Java, or Go

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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Interviewed February 2024 · Not specified

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

A candidate interviewing for an L5 role went through a phone screening, three onsite coding rounds, a mobile system design round, and a Googleyness and Leadership round. Two of the four technical rounds went poorly by the candidate's own assessment, and after roughly two months of silence the recruiter reported that the role had been closed.

Interviewed January 2023 · Not specified

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