Taiwan
Onsite
A Little About Us
People love to shop online, and at Yahoo, we help connect hundreds of millions of users worldwide to the things they care about. Commerce is a fast-growing area for Yahoo in the U.S., and personalization plays a key role in delivering relevant and engaging experiences to our users.
We are the U.S. Commerce Recommendation Engine and Content Understanding team. Our mission is to build high-quality, reliable recommender systems that deliver personalized products and content at scale. The team works on recommendation and personalization models, content understanding and search ranking, as well as the data aggregation and processing pipelines that power these systems in production.
As we continue to grow, we are looking for senior engineer who are excited to apply strong machine learning fundamentals alongside modern, AI-augmented workflows to build and improve production-grade recommendation systems.
A Lot About You
As a member of the U.S. Commerce Recommendation Engine and Content Understanding team, you will work on applied machine learning problems at web scale, tackling real-world challenges across large e-commerce and content domains. You’ll collaborate closely with experienced engineers and product owners, leveraging internet-scale datasets and large cloud computing clusters to build systems that have a meaningful impact on Yahoo’s products and users.
We are looking for senior engineers with strong coding skills and a solid foundation in machine learning and information retrieval. In this role, you will develop recommender systems and related services using Python, Java, or Scala on Unix-based platforms, building production systems that process massive volumes of data with low-latency requirements.
If you’re excited about building high-impact, scalable machine learning systems and applying your skills to real-world products used by millions, we’d love to hear from you.
Your Focus & Impact
Technical Leadership: Lead the design and implementation of complex research engineering systems and machine learning models. You will be responsible for ensuring that systems are scalable, fault-tolerant and secure.
AI-Accelerated Engineering: Actively integrate and champion the use of AI tools (e.g., GitHub Copilot, Cursor, LLMs) to accelerate the development lifecycle from rapid prototyping and automated unit testing to code refactoring and documentation.
Operational Ownership: Embrace the "You Build It, You Run It" philosophy. You will take ownership of the full lifecycle of your systems and models, ensuring high observability, robust CI/CD pipelines, and proactive system health management.
Technical Mentorship: Elevate the research engineering and machine learning bar by conducting deep-dive design reviews, providing insightful code feedback, and mentoring junior/mid-level engineers on best practices.
Collaborative Innovation: Work seamlessly with Product Managers and Architects to translate ambiguous business requirements into concrete technical roadmaps.
Minimum Qualifications
Experience: B.S. or M.S. in Computer Science (or equivalent) with 5+ years of professional experience in software engineering, machine learning, artificial intelligence, and/or recommender systems.
Technical Depth: Strong proficiency in at least one general-purpose programming language (Python preferred; Java or Scala acceptable).
Technical Leadership: Demonstrated technical strength through contributions to system design discussions, architectural components, and implementation of machine learning use cases.
System Design: Experience designing, building, and maintaining production-grade machine learning systems, preferably in distributed and microservices-based environments.
AI Tooling Proficiency: Experience using AI-assisted coding tools to improve development velocity and code quality.
Communication: Strong analytical, structured thinking, and interpersonal skills, with the ability to collaborate effectively across teams; fluent in spoken and written English.
Preferable Job Qualifications
Python Expertise: Strong experience writing production-quality Python code, including building, testing, debugging, and maintaining scalable services and data pipelines.
End-to-End Machine Learning Development: Experience launching machine learning models from ideation through stable deployment in production environments.
LLM Expertise: Experience building and maintaining large language models in production environments.
Observability: Experience monitoring, maintaining, and troubleshooting machine learning models in production environments.
Software Development Practices: Experience working in modern development environments using Agile methodologies and CI/CD practices.
Big Data and Cloud: Experience with distributed computing technologies (Hadoop, Spark, HBase, Storm) and cloud platforms (AWS, GCP) in production environments.
Technical Mentorship: Experience mentoring junior engineers through code reviews, design discussions, and engineering best practices, supporting technical growth across the team.
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.
Yahoo has a high degree of flexibility around employee location and hybrid working. In fact, our flexible-hybrid approach to work is one of the things our employees rave about. Most roles don’t require specific regular patterns of in-person office attendance. If you join Yahoo, you may be asked to attend (or travel to attend) on-site work sessions, team-building, or other in-person events. When these occur, you’ll be given notice to make arrangements.
If you’re curious about how this factors into this role, please discuss with the recruiter.
Currently work for Yahoo? Please apply on our internal career site.