Machine Learning Engineer Intern; Monetization Technology - Ads Creative BS​/MS

Machine Learning Engineer Intern (Monetization Technology - Ads Creative) - 2026 Start (BS/MS)

Location:

San Jose

Employment Type:

Intern

Job Code: A95112

Responsibilities:

A "creative" is the ad (in the form of a short-form video) served to Tik Tok users, composed of video, background music, call-to-action card, post-click landing page, and other formats that get delivered to users. A quote goes "creativity is the soul of advertising", because a good ad creative is effective, yet difficult to produce, especially at the scale of Tik Tok advertising.

The Tik Tok Ads Creative & Ecosystem team's mission is to solve the above dilemma, by building industry-leading tech solutions for ads creative/landing page understanding, production/generation, and optimization, to inspire and empower advertisers, creators, and other 3rd parties in the ecosystem to create and deliver the best engaging creative experiences to the end users. Our work is at the core of Tik Tok and creator monetization.

We are user/product oriented and dedicated to technical excellence. We aim to drive and lead the technology renovation in the ads tech and creative industry, powering products and driving values for our clients, creators, and the whole ecosystem. We are excited to grow advertisers' and users' business understanding, build highly scalable and reliable software/infrastructure, partner across functions with global teams, and make big impacts.

If you are someone who welcomes challenges, we are eager to have you on the team! We are looking for talented individuals to join us for an internship in 2026. Internships at Tik Tok aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities ernships at Tik Tok aim to provide students with hands-on experience in developing fundamental skills and exploring potential career paths.

A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. It runs for 12 weeks. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply.

The application limit is applicable to Tik Tok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis. We encourage you to apply as early as possible. Please state your availability clearly in your resume (Start date, End date). Summer Start Dates:

- May 11th, 2026 - May 18th, 2026 - May 26th, 2026 - June 8th, 2026 - June 22nd, 2026 Applications will be reviewed on a rolling basis - we encourage you to apply early. Successful candidates must be able to commit to at least 3 months long internship period.

Qualifications:

Minimum Qualifications:

1. Currently pursuing a Bachelor's degree or higher in Computer Science or a related field.
2. Research/internship experience or coursework in machine learning (e.g., Rec Sys, NLP, CV, GE), with a preference for candidates with exposure to recommendation systems.
3. Solid understanding of data structures and algorithms, with proficiency in at least one programming language (e.g., Python, C++, Golang). 4. Strong interest in exploring new technologies, with a demonstrated ability to analyze problems and find solutions.
5. Good communication skills, with an eagerness to collaborate within a team and learn from peers.
6. Strong enthusiasm for contributing to business growth and willingness to take on challenges in a dynamic environment.

Preferred Qualifications:

1. Previous internship or research experience in machine learning/deep learning, with a focus on recommendation systems, or advanced ranking solution strategies like RAG/LoRA/MoE etc. 2. Familiarity with large-scale data processing and distributed systems.
3. Exposure to reinforcement learning or deep learning techniques for optimizing recommendation systems.
4. Knowledge of A/B testing and other experimental design techniques to evaluate algorithm performance.
5. Strong interest in content personalization and ad optimization technologies.
6. Experience with model deployment or working in production environments.