Software Engineer Intern (AML) - 2027 Summer

About the team

The mission of our AML team is to push the next-generation AI infrastructure and recommendation platform for the ads ranking, search ranking, live & ecom ranking in our company. We also drive substantial impact on core businesses of the company. Currently, we are looking for Software Engineer - Machine Learning Infrastructure to join our team to support and advance that mission.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.

Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.

Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

Responsibilities

• Responsible for improving the workflow of model training and serving, data pipelines, storage system and resource management for multi-tenancy machine learning systems.
• Responsible for designing and developing key components of ML infrastructure

Minimum Qualification(s)

• Currently pursuing an Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline.
• Able to commit to a 12-week internship during Summer 2027.
• Proficient coding skills and strong algorithm & data structure basis.
• Effective communication and teamwork skills.

Preferred Qualifications

• Currently pursuing an Master's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline.
• Experience in using/designing open-source machine learning lifecycle management systems: TFX
• Ability to think critically, rationally, and communicate in result-oriented, data-driven manner.