Machine Learning Engineer Intern; Data-Search- Recommendation Team - Summer; BS​/MS

Responsibilities

Team Introduction

Our Search Engineering Team is responsible for building and owning Tik Tok's search engine which can provide our users with the best search experience. In Tik Tok Search Team, you’ll have the opportunity to build full‑stack search engine system and combine information retrieval technology with modern machine learning methods from related fields such as NLP, CV, and recommender system. We embrace a culture of self‑direction, intellectual curiosity, openness, and problem‑solving.

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 at Tik Tok.

Internships 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.

We encourage you to apply early and state your availability clearly in your resume (Start date, End date).

• May 11th, 2026
• May 18th, 2026
• May 26th, 2026
• June 8th, 2026
• June 22nd, 2026

You will have the opportunity to participate in the improvement of the search core algorithm. Possible directions include:

• Content understanding:
  Applying cutting‑edge NLP and CV technology and leveraging LLM to match the most relevant videos for each search query, and continuously improve relevance of Tik Tok search.
• User Behavior Modeling:
  Solving the recommendation problem in search, let Tik Tok search increase personalization on the basis of "relevant", and understand users better.
• Video understanding:
  Comprehensive use of NLP, CV, as well as LLM for better video understanding from the perspective of the video itself and social network, improve authority, credibility, and usefulness of search results.

• Must commit to a 12‑week full‑time work period during Summer or Fall 2026.
• Currently pursuing a Bachelor's or Master's in Software Development, Computer Science, Computer Engineering, or related technical discipline.
• Proficient coding skills and strong algorithm & data structure basis.
• Effective communication and teamwork skills.
• Experience in one or more areas: NLP, Ranking, Ads, search engine, recommender system, distributed system, and machine learning.

• Demonstrated software engineering experience from previous internship, work experience, coding competitions, or publications.
• High levels of creativity and quick problem‑solving capabilities.

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here:

Compensation (Hourly) - Campus Intern: $42.75‑$42.75. Benefits may vary depending on the nature of employment and the country work location. Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year).

Interns who are not working 100% remote may also be eligible for housing allowance. The company reserves the right to modify or change these benefits programs at any time, with or without notice.

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or…