Senior AI Engineer - Monetization Platform

Yahoo serves as a trusted guide for hundreds of millions of people globally, helping them achieve their goals online through our portfolio of iconic products. For advertisers, Yahoo Advertising offers omnichannel solutions and powerful data to engage with our brands and deliver results.

A Little About Us

We are an industry-leading direct-to-consumer and ad tech solution for advertisers and publishers. Our innovative ad tech gives one-stop access to Yahoo, Inc.’s trusted data, high-quality inventory and demand, creative ad experiences, and industry-leading machine learning at global scale.

The Consumer Monetization team’s charter is to Find, Evaluate, Build, and Scale new monetization, subscription, and internal campaign tools and products, ad formats and functionalities across all Yahoo brands including Yahoo Homepage, Yahoo Sports, Yahoo Finance, Yahoo News, and AOL. This team is uniquely positioned to identify growth and revenue generation opportunities, design and implement solutions across consumer products and advertising platforms including video, display, native, and search.

A Lot About You

As part of the Consumer Monetization Platform Engineering team, you will be the technical lead for building production-grade ML/AI models and inference systems that power our measurement intelligence platform. You will design and implement causal measurement models (brand uplift, sales uplift), multi-touch attribution systems (path to conversion), and AI agent architectures that automate advertising intelligence at scale.

You are a rare hybrid: you have the data science foundation to design statistically rigorous experiments and build sophisticated models, AND the software engineering skills to ship those models as production services. You think in PyTorch and Pydantic, you build agentic workflows with LangChain/LangGraph and Google ADK, and you leverage knowledge graphs and GraphRAG to give AI systems structured reasoning over complex advertising data.

Our Big Data footprints are among the largest few in the world, at double-digit petabyte scale. Developing ML systems at this scale presents challenges in efficient feature engineering, distributed model training, low-latency inference, and building AI agents that operate reliably in high-stakes revenue environments.

If you are someone who is commitment to building ML systems that directly drive business outcomes, enjoys bridging the gap between research and production, and wants to shape the future of AI-powered advertising—we want to hear from you!

Your Day

• Design, train, and deploy brand uplift models using causal inference techniques (difference-in-differences, synthetic control groups, propensity score matching) to measure advertising effectiveness for brand campaigns

• Build sales uplift and ROAS measurement systems that connect ad exposure to downstream conversion and purchase events, enabling closed-loop attribution reporting for performance advertisers

• Develop multi-touch attribution and path-to-conversion models using Markov chains, Shapley values, and deep learning approaches to accurately value impressions across the full consumer journey

• Design and implement feature engineering pipelines—from raw event data through feature computation, storage, and real-time serving—that power all measurement and optimization models

• Build and optimize model training pipelines using PyTorch, with experiment tracking, hyperparameter tuning, and automated retraining workflows on large-scale advertising datasets

• Deploy models as low-latency inference services using Vertex AI, with Pydantic-based API contracts, model versioning, A/B testing, and canary deployment patterns

• Build agentic AI systems using LangChain, LangGraph, and Google Agent Development Kit (ADK) for autonomous advertising intelligence—including yield optimization agents, publisher intelligence tools, and measurement reporting agents

• Design and implement knowledge graph-powered reasoning systems using GraphRAG architectures that enable AI agents to reason over structured advertising data, audience relationships, and campaign context

• Develop contextual bandit and reinforcement learning agents for dynamic yield optimization, including floor pricing, header bidding configuration, and demand partner allocation

• Build behavioral embedding models that transform raw user signals into dense vector representations for audience intelligence, lookalike modeling, and real-time targeting

• Collaborate with