Ship GenAI research into production ML pipelines at Google DeepMind

This Google DeepMind Software Engineer role (Sunnyvale, hybrid) sits at the boundary of research and production: prototyping GenAI solutions for generative media and multimodal understanding, building ML pipelines, and hardening product code with integration/performance/security testing. It requires 2 years of experience training generative AI models for media generation, building models in TensorFlow/PyTorch/JAX, managing ML infrastructure (deployment, evaluation, optimization, data processing), and general software development in Java/C/C++/Python/Go. Expect the mock to blend ML system design with core coding fundamentals (data structures/algorithms) and production-engineering rigor.

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What this interview tests

Training and evaluating generative AI models for media generation
ML framework tradeoffs (TensorFlow, PyTorch, JAX)
ML infrastructure: deployment, evaluation, optimization, data pipelines
Core data structures and algorithms
Debugging and root-causing production system issues (performance, security, reliability)

Common question themes

Design an ML pipeline to take a generative media model from prototype to production

Compare TensorFlow, PyTorch, and JAX for a training workload — when would you pick each

Walk through debugging a complex production issue in an ML-serving system

How would you structure integration and performance tests for a generative model service

Solve a data structures/algorithms problem and discuss complexity tradeoffs

How candidates describe it

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

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