ML Engineer Intern – Search & Relevance (PhD)

TikTok; San Jose, Northern (CA, KY); Hybrid; USD 68,000 - 97,000; Part time

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

10 paid holidays per year

Housing allowance

Job summary

TikTok is offering a Machine Learning Engineer Intern position in San Jose for a 12-week term, targeting PhD students. You will work on the Search team to design, implement, and test features that improve content discovery using ML, NLP, and large-scale data processing.

The role emphasizes collaboration with cross-functional partners, hands-on learning, and professional development within a dynamic internship program.

Qualifications

• Must be able to commit to a 12-week full-time internship.
• PhD coursework or recent PhD in Software Development, Computer Science, Computer Engineering, or related field.
• Strong coding skills and solid understanding of algorithms & data structures.
• Excellent communication and teamwork abilities.
• Experience in NLP, ranking, ads, search engine, recommender systems, distributed systems, or ML preferred.

Responsibilities

• Support design, implementation, testing, and iteration of Search features and services.
• Work with large-scale data, logs, metrics, and experiments to understand search behavior.
• Collaborate with engineering, product, data science, and ML teams to meet project milestones.
• Write clear, maintainable code and documentation for assigned workstreams.
• Communicate progress, risks, and learnings with mentors and stakeholders.

Skills

Python

Machine learning

NLP

Distributed systems

Tools

TensorFlow

SQL

Pandas

Job description

TikTok is offering a Machine Learning Engineer Intern position in San Jose for a 12-week term, targeting PhD students. You will work on the Search team to design, implement, and test features that improve content discovery using ML, NLP, and large-scale data processing.

The role emphasizes collaboration with cross-functional partners, hands-on learning, and professional development within a dynamic internship program.