Skip to content
AI Engineer Jobs
Google

Technical Lead Manager, Data Infra, Gemini AutoCloud, Agentic AI

Google

Location
Onsite (Sunnyvale, CA)
Compensation
$207k - $300k/yr
Employment
Full-time
Level
Senior Level
Posted 2 days ago

About the Role

Join Google Cloud's Gemini AutoCloud team to build the Quality Flywheel platform, a scalable infrastructure for autonomous agentic AI workflows. As Technical Lead Manager, you will architect core systems and lead a team of engineers to drive innovation in Gen AI and distributed storage.

Skills

Software Development Machine Learning Distributed Storage People Management Team Leadership Gen AI Agent Frameworks RAG Prompt Engineering Model Deployment Model Evaluation Data Processing Debugging Fine Tuning Mentorship Communication

Benefits

  • Health Insurance

Perks

  • Bonus
  • Equity

Full job details

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 8 years of experience in software development.
  • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  • 3 years of experience with distributed storage.
  • 2 years of experience in a people management or team leadership role.

Preferred qualifications:

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
  • Experience with technical innovation within cross-functional engineering environments.
  • Experience in 0-1 projects.
  • Expertise building Gen AI, ML products, Agent frameworks, Agent protocols (ex: MCP, A2A), RAG, Prompt Engineering, Evals.
  • Strong track record of mentorship and excellent communication skills.

About the job:

Gemini Cloud Assist is Google Cloud’s AI powered cloud management product portfolio that transforms the experience of our customers across all surfaces from learn, design, deploy, operate, investigate and optimize their applications, workloads and services. Gemini Cloud Assist was highlighted in Cloud Next 2026 keynotes. The AI space is moving very fast, in the Agentic era, we're extending Gemini Cloud Assist to Autonomous Cloud with agentic AI. The goal is to build a fully autonomous agent to power all AI workflows for all GCP customers. It's a big bet for Google Cloud! Our team is responsible for building the Quality Flywheel which is the underlying infra/platform to supercharge Cloud Assist, AutoCloud, Cloud Support with a highly scalable, autonomous, extensible, easy to use platform! We are an agile, fast-paced, cross-functional group focused on delivering high-impact features that have super high executive-level visibility.

In this role, you will be the overall lead for the Data Infra pillar. As a core TL of the Quality Flywheel team, you will work on a large-scale distributed system that is critical for the Agentic AI adoption. You will drive core infra pieces (Context and Memory, Recursive Self-improvement, Agentic AI Workflow, GCP Quality Gate, Gemini Upstreaming, GCA Feature Store, etc.). Your work is a combination of Infra, Quality, Agentic AI. Gemini Cloud Assist is expanding rapidly as a very core foundation for Cloud's AI initiative AutoCloud. You will have the opportunity to influence and work across Google Cloud, Google 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:

  • Lead and manage a team of 3–6 engineers, guiding project execution, driving on-call and operational excellence, and mentoring junior engineers to foster technical growth and a high-performance team culture.
  • Architect, design, and implement end-to-end components for the Quality Flywheel including Agentic AI, Context and Memory, and Hill Climbing engines while upholding project-wide software and system design standards.
  • Build and scale robust machine learning infrastructure and platforms to power critical workflows such as large-scale data collection, Gemini post-training, unified benchmarking, and next-generation evaluation systems.
  • Define and own the overarching testing architecture and reliability standards to guarantee high availability, fault tolerance, and system quality across production and test environments.
  • Partner with cross-functional stakeholders to translate complex, multi-team requirements into simplified, scalable technical architectures and actionable roadmaps.