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 data scientists, product managers, and platform engineers to translate business problems into ML solutions with measurable impact

• Establish ML observability: model performance monitoring, drift detection, automated alerting, and continuous improvement loops for all production models

• Lead technical design reviews and mentor team members on ML engineering best practices, model architecture decisions, and production deployment patterns
   

Qualifications

• BS with 7+ years of relevant industry experience, or M.S./Ph.D. in Computer Science, Statistics, Machine Learning, or a related quantitative field with 5+ years of relevant industry experience.

• Strong foundations in machine learning, statistical modeling, causal inference, and experimental design

• 5+ years of experience building and deploying production ML systems (not just research/prototyping)—from feature engineering and model training through inference serving and monitoring

• Proficiency in Python with strong software engineering practices: PyTorch for model development, Pydantic for data validation and API contracts, and production-quality code with testing and CI/CD

• Experience with LLM application development frameworks: LangChain, LangGraph, or equivalent agent orchestration frameworks for building multi-step AI workflows

• Experience with cloud ML platforms, preferably Google Cloud (Vertex AI, BigQuery ML, Dataflow) for training, serving, and managing ML models at scale

• Experience with feature engineering and feature store patterns for large-scale ML systems

• Proficiency in SQL and experience working with petabyte-scale data warehouses (BigQuery, Spark, etc.)

• Experience with deep learning architectures: embeddings, transformers, sequence models, or graph neural networks

• Strong understanding of A/B testing, uplift modeling, and causal inference methodologies

• Self-driven, challenge-loving, detail-oriented, with excellent communication skills and the ability to translate complex ML concepts for cross-functional stakeholders
   

Nice to Have

• Experience with Google Agent Development Kit (ADK) or similar agent-native development frameworks

• Experience with knowledge graphs, graph databases (Neo4j, Spanner Graph), and GraphRAG architectures for structured reasoning

• Experience with contextual bandits, reinforcement learning, or multi-armed bandit algorithms in production environments

• Experience in ad tech, programmatic advertising, or publisher-side monetization (measurement, attribution, yield optimization)

• Experience with causal ML methods: difference-in-differences, synthetic control, instrumental variables, propensity score methods

• Experience with privacy-enhancing technologies, differential privacy, federated learning, or clean room computation

• Experience with MLOps tooling: MLflow, Kubeflow, Weights & Biases, or Vertex AI Pipelines

• Background in NLP, information retrieval, or recommendation systems

• Experience with distributed training (Horovod, DeepSpeed, FSDP) for large-scale models
   

Strategic Alignment

This initiative aligns with our business goals and positions us for sustained success in the evolving digital advertising landscape to support our O&O publisher sites to increase their Revenue and profitability. The measurement intelligence models this engineer will build are the competitive moat that differentiates our platform—proving advertising effectiveness through causal measurement, optimizing revenue through learned policies, and enabling the agentic AI future where intelligent agents automate the full media buying and selling lifecycle.

The material job duties and responsibilities of this role include those listed above as well as adhering to Yahoo policies; exercising sound judgment; working effectively, safely and inclusively with others; exhibiting trustworthiness and meeting expectations; and safeguarding business operations and brand integrity.

At Yahoo, we offer flexible hybrid work options that our employees love! While most roles don’t require regular office attendance, you may occasionally be asked to attend in-person events or team sessions. You’ll always get notice to make arrangements. Your recruiter will let you know if a specific job requires regular attendance at a Yahoo office or facility. If you have any questions about how this applies to the role, just ask the recruiter!

Yahoo is proud to be an equal opportunity workplace. All qualified applicants will receive consideration for employment without regard to, and will not be discriminated against based on age, race, gender, color, religion, national origin, sexual orientation, gender identity, veteran status, disability or any other protected category. Yahoo will consider for employment qualified applicants with criminal histories in a manner consistent with applicable law. Yahoo is dedicated to providing an accessible environment for all candidates during the application process and for employees during their employment. If you need accessibility assistance and/or a reasonable accommodation due to a disability, please submit a request via the Accommodation Request Form (www.yahooinc.com/careers/contact-us.html) or call +1.866.772.3182. Requests and calls received for non-disability related issues, such as following up on an application, will not receive a response.

We believe that a diverse and inclusive workplace strengthens Yahoo and deepens our relationships. When you support everyone to be their best selves, they spark discovery, innovation and creativity. Among other efforts, our 11 employee resource groups (ERGs) enhance a culture of belonging with programs, events and fellowship that help educate, support and create a workplace where all feel welcome.

The compensation for this position ranges from $128,250.00 - $266,875.00/yr and will vary depending on factors such as your location, skills and experience.The compensation package may also include incentive compensation opportunities in the form of discretionary annual bonus or commissions. Our comprehensive benefits include healthcare, a great 401k, backup childcare, education stipends and much (much) more.

Currently work for Yahoo? Please apply on our internal career site.