Machine Learning Engineer Intern (TikTok-Data-Search-Recommendation) - 2027 Summer

San Jose, California, US Until 10/3/2026 H-1B sponsor history First posted August 4, 2026 Last posted August 4, 2026
Job description

Description

The Search team builds the systems that help users find relevant, reliable, and engaging content across TikTok. The team works across search retrieval, ranking, recommendation signals, query understanding, relevance, infrastructure, and product experiences. Our work combines large-scale machine learning, data-driven product development, and distributed systems to improve search quality for a global user base.

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.

Responsibilities
- Support the design, implementation, testing, and iteration of Search features, services, or tools under the guidance of engineers and mentors.
- Work with large-scale data, logs, metrics, or experiments to understand user search behavior and improve product or system quality.
- Collaborate with engineering, product, data science, and machine learning partners to define requirements and deliver project milestones.
- Write clear, maintainable code and documentation for assigned workstreams.
- Communicate progress, risks, and learnings with mentors and stakeholders throughout the internship.

Requirements

Minimum Qualifications
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, Statistics, Mathematics, or a related technical discipline.
- Experience with at least one programming language such as Python, Java, C++, Go, or JavaScript through coursework, projects, internships, or equivalent practical experience.
- Familiarity with data structures, algorithms, software engineering fundamentals, and basic system design concepts.

Preferred Qualifications
- Experience with search, recommendation, machine learning, information retrieval, natural language processing, ranking, ads, content understanding, or data mining projects.
- Familiarity with databases, distributed systems, data pipelines, experimentation, or large-scale backend services.
- Experience using data to investigate product, user, or system problems.
- Ability to communicate technical ideas clearly and collaborate with cross-functional partners.
- Interest in improving search experiences for large-scale consumer products.

As a condition of employment, all successful candidates must be able to establish authorization to work in the United States. For this position, the Company does not provide sponsorship or any immigration-related benefits.

About this role

Summary

Assist in designing and implementing search features using large-scale data and machine learning.

Job title

Machine Learning Engineer Intern (TikTok-Data-Search-Recommendation) - 2027 Summer

Experience level

internship

Industry

software

Location requirements

San Jose, remote work not allowed

Salary

Not specified

Visa sponsorship

H-1B sponsor history

Management role

No

Skills & keywords

Required skills

Pythondata structuresalgorithmssystem design

Preferred skills

searchrecommendationmachine learningnatural language processingdistributed systems

Specializations

machine learningsearchrecommendationnatural language processing
Locations

Structured locations inferred from the posting.

San Jose, CA, USA

On-site City