The Muse
Newsletter Sign In
Jobs ▾
SalesSoftware EngineeringOperationsFinanceMarketingPeople & HRData ScienceDesignHealthcareEducationView All Jobs
CompaniesResume BuilderAdviceCoachingNewsletter EmployersSign In
TikTok

(General Hire) Machine Learning Engineer Intern (TikTok-Recommendation) - 2027 Summer

Aug 3, 2026• San Jose

About us

TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.

  • Size: 5,001 - 10,000 employees
  • Industry: Entertainment & Gaming
View company profile ›
Responsibilities

Recommendation algorithm team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is fast-pacing, collaborative and impact-driven.

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.

* This opening is part of the general hiring process for the TikTok Recommendation organization. Applications will be evaluated by multiple teams within the Recommendation organization to ensure the best alignment based on skills and interests.

In this role, you'll have the opportunity to:
- Drive the development of industry-leading recommendation systems that elevate user experience, strengthen platform safety, and empower a vibrant content ecosystem.
- Deliver impactful, end-to-end machine learning solutions that tackle high-priority product challenges related to content understanding, LLMs, robustness, and fairness.
- Own and optimize the full-stack ML pipeline-from algorithm design to system infrastructure-to continuously push the boundaries of recommendation performance.
- Collaborate with cross-functional teams to craft innovative product strategies and develop intelligent solutions that fuel TikTok's growth in key global markets.

Qualifications

Minimum Qualifications:
- Currently pursuing a Master degree with a background in computer science, machine learning, or similar fields;
- Good knowledge of theoretical and empirical research in addressing research problems;
- Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch,TensorFlow) and familiarity with deep neural network architectures.
- Able to commit to working for 12 weeks during Summer 2027

Preferred Qualifications:
- Research experience in one or more of the following fields: applied machine learning, machine learning infrastructure, large-scale recommendation system, market-facing machine learning product;
- Strong first-author publications record in top AI conferences or journals(e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.);
- Proficient in C/C++, Python, and shell programming languages, and have a deep understanding of data structure and algorithm design;
- Internship experience in an AI research organization.

For TikTok
By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

Responsibilities

Responsibilities

Recommendation algorithm team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is fast-pacing, collaborative and impact-driven.

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.

* This opening is part of the general hiring process for the TikTok Recommendation organization. Applications will be evaluated by multiple teams within the Recommendation organization to ensure the best alignment based on skills and interests.

In this role, you'll have the opportunity to:
- Drive the development of industry-leading recommendation systems that elevate user experience, strengthen platform safety, and empower a vibrant content ecosystem.
- Deliver impactful, end-to-end machine learning solutions that tackle high-priority product challenges related to content understanding, LLMs, robustness, and fairness.
- Own and optimize the full-stack ML pipeline-from algorithm design to system infrastructure-to continuously push the boundaries of recommendation performance.
- Collaborate with cross-functional teams to craft innovative product strategies and develop intelligent solutions that fuel TikTok's growth in key global markets.

Qualifications

Qualifications

Minimum Qualifications:
- Currently pursuing a Master degree with a background in computer science, machine learning, or similar fields;
- Good knowledge of theoretical and empirical research in addressing research problems;
- Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch,TensorFlow) and familiarity with deep neural network architectures.
- Able to commit to working for 12 weeks during Summer 2027

Preferred Qualifications:
- Research experience in one or more of the following fields: applied machine learning, machine learning infrastructure, large-scale recommendation system, market-facing machine learning product;
- Strong first-author publications record in top AI conferences or journals(e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.);
- Proficient in C/C++, Python, and shell programming languages, and have a deep understanding of data structure and algorithm design;
- Internship experience in an AI research organization.

For TikTok
By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

Want more jobs like this?

Get jobs in San Jose delivered to your inbox every week.

By signing up, you agree to our Terms of Service & Privacy Policy.

Additional Information

  • Posted: Aug 3, 2026
Apply on company site
Apply on company site

Suggested Searches

  • TikTok Jobs
  • All Jobs

Search Additional Jobs

  • Remote-Friendly (General Hire) Machine Learning Engineer Intern (TikTok-Recommendation) - 2027 Summer Jobs
  • San Jose Jobs
The Muse

© 2026 FGB Muse Group Inc.

114 Rayson Street, 1st Floor
Northville, MI 48167

About The Muse

  • About Us
  • FAQs
  • Search Jobs
  • Browse Companies
  • Career Advice
  • Terms of Use
  • Privacy Policy
  • Contact Us
  • FairyGodBoss

Popular Jobs

  • New York Jobs
  • San Francisco Jobs
  • Seattle Jobs
  • Engineering Jobs
  • Marketing Jobs
  • Information Technology Jobs

Get Involved

  • For Employers
  • The Muse Book: The New Rules of Work
  • For Career Coaches
  • Tell A Friend

Join The Conversation