ML Engineer Intern: Recommendations & Search (Summer 2027)

InternHunt; San Jose, Northern (CA, KY); Hybrid; USD 51,000 - 73,000; Full time

Benefits offered by this job

Housing allowance

Health insurance

Job summary

TikTok USDS Joint Venture LLC seeks a Machine Learning Engineer Intern for Summer 2027 in San Jose, CA. You will design, train, and evaluate ML models for recommendations and search, build scalable pipelines, and work with large-scale user data to deliver personalized experiences while upholding data governance and privacy standards.

This 12-week internship offers hands-on experience, collaboration with a multidisciplinary team, and opportunities to learn about real-world ML systems, deployment,

Qualifications

• Currently pursuing a Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical field.
• Able to commit to a 12-week internship during Summer 2027.
• Proficient coding skills and strong algorithm & data structure basis.
• Effective communication and teamwork skills.

Responsibilities

• Design, build, train, and evaluate machine learning models for recommendation systems or search ranking.
• Implement efficient pipelines for feature extraction, model training, evaluation, offline-to-online deployment, and A/B testing.
• Process and analyze massive user behavior data and catalog logs using distributed systems to construct real-time and offline features.

Skills

Coding skills

Algorithms & data structures

Communication & teamwork

Education

Bachelor's degree in CS or related field

Master's degree preferred

Tools

scikit-learn

TensorFlow

PyTorch

Job description

TikTok USDS Joint Venture LLC seeks a Machine Learning Engineer Intern for Summer 2027 in San Jose, CA. You will design, train, and evaluate ML models for recommendations and search, build scalable pipelines, and work with large-scale user data to deliver personalized experiences while upholding data governance and privacy standards.

This 12-week internship offers hands-on experience, collaboration with a multidisciplinary team, and opportunities to learn about real-world ML systems, deployment,