PhD Research Scientist Intern: Build Next-Gen Recommenders

TikTok; San Jose (CA); On-site; USD 68,000 - 97,000; Full time

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

Paid holidays

Paid sick time

Housing allowance

Job summary

TikTok is seeking PhD interns in the San Jose area to contribute to our industry-leading recommendation system. You will work with machine learning researchers and engineers to advance production models, improve user experience, and impact platform growth.

This internship offers hands-on learning, collaboration across teams, and exposure to cutting-edge research. Candidates should state their availability in resume; applications reviewed on rolling basis.

Qualifications

• Pursuing a PhD in computer science or related field.
• Proficient coding in C++/Python or similar.
• Experience in Machine Learning with at least one DL framework.
• Experience in NLP, CV, Recommender System, and ML domains.
• Strong communication and teamwork skills.

Responsibilities

• Build industry-leading recommendation system, improving user experience, content ecosystem and platform security.
• Deliver end-to-end machine learning solution to address critical product challenges.
• Own the full stack machine learning system and optimize algorithms and infrastructure.
• Work with cross functional teams to design product strategies and build solutions to grow TikTok in important markets

Skills

C++/Python programming

Machine Learning

Deep Learning frameworks

NLP/CV/RS ML

Communication & teamwork

Education

PhD in computer science or related field

Job description

TikTok is seeking PhD interns in the San Jose area to contribute to our industry-leading recommendation system. You will work with machine learning researchers and engineers to advance production models, improve user experience, and impact platform growth.

This internship offers hands-on learning, collaboration across teams, and exposure to cutting-edge research. Candidates should state their availability in resume; applications reviewed on rolling basis.