ML Engineer Intern - E-Commerce Growth & Personalization

TikTok; Seattle (WA); On-site; USD 49,000 - 70,000; Part time

Benefits offered by this job

Health insurance from day one

10 paid holidays per year

Housing allowance

Job summary

TikTok invites an Machine Learning Engineer Intern (E-Commerce User Growth) for the 2027 Summer in Seattle. You will contribute to the E-commerce Recommendation and Marketing Algorithm team, tackling ML-driven growth, user personalization, and scalable data systems.

Responsibilities span optimizing core growth algorithms, building value systems for user lifecycles, and deploying models to boost DAU, GMV, and retention. Strong ML foundations and passion for frontier techniques are essential.

Qualifications

• Pursuing an undergraduate degree in software development, CS, or related field.
• Solid ML and engineering fundamentals; clean, efficient code with strong data structures.
• Experience or coursework in LLMs/foundation models, NLP, CV, RL, or recommendation/search/ads.

Responsibilities

• Participate in optimizing TikTok e-commerce growth and marketing algorithms.
• Establish user lifecycle data and value systems for TikTok Mall growth.
• Contribute to product and technical solutions for user growth engines (personalized push notifications/emails).
• Optimize recall/sorting for new user recommendations; improve relevance and diversity.
• Improve marketing efficiency with uplift models and operations research methods.

Skills

ML fundamentals

Strong coding

Research experience

LLMs

NLP

CV

RL

Recommendation systems

Data structures

Education

Undergraduate degree in CS/Software/Engineering

Job description

TikTok invites an Machine Learning Engineer Intern (E-Commerce User Growth) for the 2027 Summer in Seattle. You will contribute to the E-commerce Recommendation and Marketing Algorithm team, tackling ML-driven growth, user personalization, and scalable data systems.

Responsibilities span optimizing core growth algorithms, building value systems for user lifecycles, and deploying models to boost DAU, GMV, and retention. Strong ML foundations and passion for frontier techniques are essential.