Machine Learning Engineer Intern (Brand Ads) - 2027 Summer

Location: San Jose, California, United States of America Department: R&D

The Brand Innovation Team builds technologies that unlock business growth potential. This team owns several ad products: reservation ads, auction ads, and innovative content ads that enable advertisers and users to foster more awareness of their brand to attain their business goals. We work on the end-to-end ad delivery tech stack, including ad bidding, ranking, and forecasting.

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.

Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.

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

• Build and optimize the delivery stack across pacing, allocation, traffic strategy, and delivery controls.
• Drive products from concept to launch with Product, Strategy, and cross-functional partners.
• Turn business goals into scalable delivery strategies and system designs.
• Own technical design, implementation, experimentation, launch, and iteration.
• Use data and experiments to diagnose problems and drive fast iteration.

Minimum Qualifications

• Currently pursuing a Bachelor's degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Strong coding, debugging, and system design fundamentals.
• Proficiency in Go, C/C++, Java, Python, or similar languages.
• Ability to solve ambiguous problems and make clear engineering trade-offs.
• Strong cross-functional communication and collaboration skills.
• Experience with large-scale backend, recommendation, or advertising systems.
• Hands-on ads delivery experience in pacing, bidding, auction, ranking, forecasting, or marketplace optimization is preferred.

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