Senior Machine Learning Engineer

It takes powerful technology to connect our brands and partners with an audience of hundreds of millions of people. Whether you’re looking to write mobile app code, engineer the servers behind our massive ad tech stacks, or develop algorithms to help us process trillions of data points a day, what you do here will have a huge impact on our business—and the world.

A Little About Us:

The Yahoo! Consumer Data Team is developing a unified, cloud-native platform for all Yahoo user data. We simplify how teams access and utilize Yahoo first-party and third-party data while strengthening compliance. Our work empowers business units to enhance experimentation, monetization, marketing, and personalization with greater efficiency and reduced risk.

A Little About You:

The ideal candidate will have solid experience in machine learning engineering, with demonstrated ability to implement and optimize ML systems in production environments. They should be passionate about solving real-world problems with data and ML, and skilled at collaborating with cross-functional teams including data scientists, product managers, and other engineers.

As part of our Audience Platform, you'll play a key role in helping Yahoo leverage first-party and third-party data to build comprehensive audience solutions essential for experimentation, monetization, marketing, and personalization. This large-scale initiative offers you the chance to tackle significant technical challenges while making impactful contributions to how we responsibly utilize data assets across our ecosystem.

Responsibilities:

• ML Implementation: Develop and optimize  end-to-end ML solutions that solve business problems efficiently at scale

• Production Deployment: Build reliable pipelines for training, evaluating, and deploying ML models to production environments

• Model Performance: Implement monitoring solutions to track model performance and data quality in production

• Feature Engineering: Design and develop robust features for machine learning models using large-scale data processing frameworks

• Tune, validate, and optimize models for accuracy, efficiency, and scalability, including hyperparameter optimization and performance benchmarking

• Cross-team Collaboration: Work effectively with data scientists to operationalize research models and with product teams to integrate ML capabilities

• Technical Documentation: Create comprehensive documentation for ML systems, models, and processes

• Best Practices: Apply ML engineering best practices including version control, testing, and reproducibility to all projects

• Model Optimization: Improve model efficiency, latency, and resource utilization for production environments

• Problem Solving: Troubleshoot and resolve issues with data pipelines and model serving infrastructure

• Knowledge Sharing: Participate in technical discussions and share knowledge with the broader engineering organization

Requirements:

• BS or MS in Computer Science, Data Science or a related major, or equivalent experience

• 5+ years of software engineering experience, with at least 3+ years focused on machine learning engineering

• 2+ years of experience implementing and deploying ML models to production environments

• 2+ years hands-on experience with Google Cloud Platform ecosystem (BigQuery, Dataproc, Composer, Dataflow, Data Catalog, Observability) or AWS equivalent and their ML services

• ​​Solid programming skills in Python and/or Java.

• Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn

• Solid understanding of data processing frameworks (Spark, Beam, or equivalent)

• Strong knowledge of ML fundamentals: supervised/unsupervised learning, evaluation metrics, feature engineering

• Proficiency in SQL and working with large datasets

• Demonstrated ability to translate business requirements into technical ML solutions

• Experience collaborating with data scientists and product teams

The material job duties and responsibilities of this role include those listed above as well as adhering to Yahoo policies;