ML Engineer Intern – Data-Search & Scale

TikTok; San Jose (CA); On-site; USD 68,191 - 97,120; Full time

Benefits offered by this job

Health insurance

Paid holidays

Housing allowance

Job summary

TikTok in San Jose is seeking a Machine Learning Engineer Intern (PhD) for a 12-week full-time internship starting 2026. You will design, implement and test Search features under mentor guidance, analyze large-scale data to improve search quality, and collaborate with cross-functional teams to define requirements and milestones.

You'll write clean code and documentation, communicate progress and learnings, and gain hands-on experience in NLP, ranking, and ML within a fast-paced product

Qualifications

• Pursuing a PhD in a technical field as described in the posting.
• Able to commit to a 12-week full-time internship.
• Strong coding skills and solid algorithm/data-structure foundation.
• Effective communication and ability to work in a team.
• Experience or interest in NLP, ranking, ads, search, ML, or distributed systems.

Responsibilities

• Support design, implementation, testing, and iteration of Search features under mentor guidance.
• Work with large-scale data, logs, metrics, or experiments to improve product quality.
• Collaborate with engineering, product, data science, and ML partners to define requirements and milestones.
• Write clear, maintainable code and documentation for assigned workstreams.
• Communicate progress, risks, and learnings with mentors and stakeholders.

Skills

Algorithm & data structures

Proficient coding

Communication & teamwork

NLP/ ML/Ranking experience

Education

PhD in Computer Science/Software/Engineering

Job description

TikTok in San Jose is seeking a Machine Learning Engineer Intern (PhD) for a 12-week full-time internship starting 2026. You will design, implement and test Search features under mentor guidance, analyze large-scale data to improve search quality, and collaborate with cross-functional teams to define requirements and milestones.

You'll write clean code and documentation, communicate progress and learnings, and gain hands-on experience in NLP, ranking, and ML within a fast-paced product