Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience programming in Python or C .
- 1 year of experience with one or more of the following: generative AI, reinforcement learning (e.g., sequential decision making), large language modeling, model processing, or specialization in another ML field.
- 1 year of experience with end to end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience developing accessible technologies.
About the jobOur team builds the AI-powered automation at the heart of Google Ads. Keywordless Search Ads powers multiple key products, including Dynamic Search Ads, AI Max, Performance Max (PMax), and Smart Campaigns, that help advertisers connect with users effectively and efficiently. We have an ambitious plan to significantly grow the impact of automated targeting in Search ads in the following years.
Keywordless Ads is a search-ads automation system that delivers highly relevant ads to users with minimal effort from advertisers. As a member of our team, you will solve challenging, open-ended problems in a fast-paced environment. Your work will have a direct and critical impact on advertisers and users worldwide.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Write product or system development code.
- Develop and deploy new ML/LLM models to make direct impact to Google, advertisers and users.
- Handle complex, open-ended engineering problems related to ad relevance, query understanding, and large-scale system optimization.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
