Senior ML 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.

A Lot About You

We seek a Senior Machine Learning Engineer who thrives in a collaborative environment and is driven by a challenge. Ideal candidates should have a solid background in machine learning engineering and a knack for end-to-end solution development, from model conception to deployment in production. You should be inherently curious about the systems you work with and possess a strong ability to communicate technical concepts across various teams. A bias for action and a customer-focused mindset are essential, as you will be responsible for translating complex business requirements into robust technical solutions. You are customer-focused, regardless of whether the customer is an external user or an internal team.

Responsibilities

• Design, develop, and maintain advanced machine learning models and systems, specifically focusing on ranking, recommendation, and content understanding to enhance the user experience on digital platforms.

• Collaborate with cross-functional teams, including product managers, data scientists, and software engineers, to integrate machine learning solutions into the broader product infrastructure.

• Monitor and evaluate the performance of machine learning systems in production, utilizing metrics to guide improvements and ensure optimal operation.

• Drive the development and implementation of MLOps practices, ensuring scalability and efficiency of machine learning workflows.

Qualifications

• Bachelor’s or Master’s in Data Science, Mathematics, Statistics, Economics, Computer Science, or a related field.

• 8+ years of software engineering experience 

• Strong expertise in machine learning, NLP, and recommender systems for content classification and personalization.

• Proficiency in Python, Java, or Scala, and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).

• Experience working with large-scale datasets, distributed computing (Spark, Hadoop), and cloud platforms (AWS, GCP, or Azure).

• Experience with continuous integration/continuous deployment (CI/CD) practices.

• Strong understanding of metadata extraction, named entity recognition (NER), and semantic search technologies.

• Proven ability to scale and optimize ML models for production environments.

• Solid foundation in machine learning operations (MLOps), with experience in automating machine learning workflows in production environments.

Preferred

• Publications or presentations on recommendation systems in leading conferences or journals are a strong plus.

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 applicatio