About the job
Our mission is to accelerate ML innovation and revolutionize ad policy enforcement through a scalable, automated platform. Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google.
Responsibilities
Manage a team of engineers who are focused on: enhancing and architecting the rollout of architecture across all new and refreshed workhorse models, solving the bottlenecks of traditional monolithic models and allowing independent, parallel policy head updates.
Drive the migration of LLM Boosters to the low-latency serving platform to dramatically reduce the time harmful ads are live (from >24 hours to ~15 minutes or real time).
Lead our agentic solutions toolkit to automate the model training lifecycle, utilizing AI agents to evaluate models and optimize prompts to completely remove human raters from the critical path.
Support the transition from complex, multi-layered FLAMeD fine-tuning to In-Context Learning (ICL) on Base Gemini models.
Scale state-of-the-art agile response platforms to onboard the next wave of high-risk policies.
Qualifications
Minimum
Bachelor’s degree or equivalent practical experience.
8 years of experience building AI-powered products, featuring technologies such as long context windows, Retrieval Augmented Generation (RAG), or similar.
Experience building and architecting production quality Machine Learning (ML) systems.
Experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
Experience building production quality ML systems.
2 years of experience in a people management, supervision/team leadership role.
Preferred
Experience in software engineering, applied ML, autonomous systems, and large-scale systems design.
Experience in machine learning, large language model, large-scale data processing, project management, Generative AI Agent, thought leadership.
Strong cross-functional team collaboration skills and experience coordinating work between partners.
Proven track record in technical project management, with the ability to drive complex, multi-quarter ML initiatives to successful completion.