Senior Principal Machine Learning Engineer

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.

It takes powerful technology to transform complex data into intelligent solutions that drive business impact at scale. Whether you're designing groundbreaking ML architectures, leading the evolution of our production AI systems, or pioneering algorithms that process trillions of data points daily, what you do here will have a transformative impact on our business—and beyond. Want in?

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 a proven track record in machine learning engineering roles, particularly in large organizations, with a strong ability to handle complex ML challenges at scale. They should be passionate about leveraging cutting-edge AI to drive business outcomes and possess excellent collaboration skills to work effectively across teams.

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 Architecture Vision: Define the technical architecture roadmap for machine learning platforms that aligns with long-term business objectives

• ML Systems Leadership: Drive innovation initiatives that span multiple engineering teams and technical domains in the ML space

• Advanced ML Design: Design distributed ML systems that process billions of events daily with industry-leading performance metrics

• ML Governance: Establish enterprise-wide standards for model quality, explainability, fairness, and compliance

• ML Innovation: Pioneer next-generation machine learning capabilities that deliver competitive advantages in user personalization and prediction

• Performance Engineering: Create frameworks and methodologies that systematically improve ML platform scalability and efficiency

• Critical ML Problem Resolution: Lead resolution of complex ML challenges that have organization-wide impact

• Executive Communication: Effectively represent ML considerations in executive-level strategy discussions

• Talent Development: Mentor senior ML engineers and technical leads to build organizational ML depth

• Industry Influence: Represent our company in ML conferences and industry forums as a thought leader

• ML Due Diligence: Lead technical evaluation of potential ML acquisitions, partnerships, and major technology investments

• ML Strategy: Anticipate emerging ML trends and guide our company's technical positioning

Requirements:

• 10+ years of software engineering experience, with at least 5+ years focused on machine learning engineering and system design

• 4+ years of experience developing large-scale machine learning systems for enterprise applications

• 3+ years hands-on experience with cloud ML ecosystems (Google Cloud AI Platform, AWS SageMaker, or Azure ML)

• Expert-level experience implementing and optimizing ML pipelines that process terabyte-scale datasets

• Deep understanding of ML fundamentals: statistical modeling, deep learning architectures, feature engineering, and model optimization

• Expert-level knowledge of ML operations including model versioning, A/B testing frameworks, online learning, and monitoring

• Advanced proficiency in Python and ML frameworks (TensorFlow, PyTorch, or JAX) with demonstrated ability to architect complex ML applications

• Experience with distributed training, model parallelism, and optimization for large models

• Track record of impleme