Technical Lead Manager, Discover Ranking
Mountain View, CA, USA · Manager · Full-time · Posted Oct 22, 2026
- Experience
- 8 years in software development; 5 years testing and launching software products; 3 years in software design and architecture; 5 years building and deploying production recommendation models; 5 years with ML design and infrastructure. Preferred: 8 years with data structures and algorithms; 3 years in technical leadership; 3 years in a complex, matrixed organization.
- Education
- Bachelor’s degree or equivalent practical experience; preferred Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
About the job
Leads engineering work on systems that personalize and rank content in Google Discover, using large-scale data and machine learning to improve recommendations.
What you'll do
- Design, develop, deploy, maintain, and enhance large-scale software solutions.
- Provide technical leadership, manage project priorities and timelines, and align teams on goals and deliverables.
- Lead recommendation-system design and implementation, optimize ML infrastructure, and guide model architecture.
What they ask for
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development; 5 years testing and launching software products; and 3 years in software design and architecture.
- 5 years building and deploying production recommendation-system models and experience building architecture in different modeling domains.
- 5 years with ML design and infrastructure, plus experience integrating generative AI tools or LLM interfaces into workflows.
Nice to have
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years with data structures and algorithms; 3 years in technical leadership; and 3 years in a complex, matrixed organization involving cross-functional or cross-business projects.
Pay
$207000 - $300000 (USD) base salary; 20% bonus target + equity + benefits (no total-pay range stated)
Tools
machine learning, recommendation systems, retrieval, prediction, ranking, embedding, generative AI, LLM interfaces
AI focus
Build and improve recommendation systems, optimize ML infrastructure, guide model architecture, and integrate generative AI tools or LLM interfaces into workflows.
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