Software Engineering, YouTube Ads Marketplace

About The Job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day.

As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Join the YouTube Ads Machine Learning team to build and scale next-generation conversion prediction models and privacy-safe measurement foundations for YouTube Demand Gen and App Ads on iOS. In this role, you will manage high-impact machine learning and attribution challenges, bridging deep learning modeling, causal/counterfactual evaluation, and privacy-preserving signals to drive revenue and advertiser return on investment (ROI) across YouTube performance ad products.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business. Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Key Responsibilities

• Design, train, and launch large-scale deep learning models (such as pCVR, pInstall, pEvent, pValue, and presence models) to improve conversion prediction accuracy and advertiser return on investment (ROI) on iOS.
• Develop and integrate privacy-preserving measurement and attribution signals.
• Diagnose and resolve complex label-bias, calibration, and counterfactual evaluation challenges across online auction and bidding systems.
• Build and maintain scalable ML training, feature engineering, and model-serving pipelines.
• Partner closely with Product Management, Research, and Ads Serving/Bidding infrastructure teams to drive projects end-to-end, from large-scale data analysis and offline prototyping to online A/B experimentation and production launches.

Requirements & Tech Stack

• Minimum qualifications:
• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages.
• 1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
• 1 year of experience with one or more of the following: reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• Experience with mathematics or analytics, with experience analyzing large-scale data.

Qualifications

• Preferred qualifications:
• Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field.
• 2 years of experience designing, training, and launching machine learning or deep learning models in production.
• Experience in ads ranking, conversion rate prediction, recommendation systems, or auction and bidding optimization.
• Experience with mobile advertising measurement, privacy-preserving attribution signals, or learning from sparse, delayed, or biased feedback.
• Ability to drive cross-functional machine learning and measurement projects end-to-end from problem formulation to production launch.