ML Engineer, GenAI Ads, Search Personalization

We are the core team responsible for Search Ads Personalization. Our personalization models and infrastructure permeate through every part of Search Ads -- from Ad Retrieval, Ranking, Auction and Creative writing. As a result, our problems are challenging both in depth (understanding nuances of user's needs), and breadth (scale across entire Google Search user base). We work on modeling user preferences and tasks using a myriad of technologies embedding models, language models, as well as building real-time and large scale inference infrastructure.

Personalizing our Ads has proven to be extremely successful, and as a result, it is a key objective of entire Ads organization. Join us to be in the centre of this personalization revolution!

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Focus on personalization (P13N) in the Generative AI space (ads on AIM, personalized ads creatives).
• Collaborate across GenAI teams in Ads Quality and GDM to create and execute on a goal/roadmap to ensure that our users feel connected with our GenAI Ads.
• Work on writing ads creatives or ad explanations to account for user tasks and preferences.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 8 years of experience with one or more general purpose programming languages (e.g., Java, C/C++, or Python).
• 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

Preferred qualifications:

• 8 years of experience with data structures and algorithms.
• 6 years of experience with Machine Learning (ML) or quality, working on recommendation systems.
• Experience in recommender systems, clustering algorithms, SQL, and deep model.
• Experience in C++, Dremel/F1, and TensorFlow.
• Experience in Research.
• Ability to drive quality projects end-to-end from design to implementation to eventual launch.