Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience integrating generative AI tools or LLM interfaces into workflows.
Preferred qualifications:- Master's degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
About the jobIn this role, you will work on a large-scale distributed system that is critical for the AI adoption for Google Cloud Platform (GCP). You will drive core infra pieces (Autocloud Simulation and Benchmark, Quality Self-improvement, Agentic Eval Workflow, GCP Quality Gate, Gemini Upstreaming, GCA Feature Store, etc.). Your work is a combination of infra, quality, AI. Gemini Cloud Assist is expanding rapidly as a very core foundation for GCP's AI initiative. You will have the opportunity to influence and work across Google Cloud, DeepMind, and the external AI ecosystem.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Design, build, and scale large-scale distributed ML infrastructure and tooling, including the Simulation and Benchmark platform, to support Gemini post-training and accelerate GCP's AI adoption.
- Lead the development of advanced AI quality components, such as agentic self-improvement, evaluation workflows, Gemini upstreaming, and automated quality gates for first-party skills.
- Lead robust software design and automated testing practices, participate in on-call rotations, and continuously optimize test and production environments for reliability and performance.
- Coach and mentor junior and mid-level engineers, enforce architectural best practices, and actively cultivate a high-performing team culture.
