Seattle ML Engineer (Graduate) – Growth & Personalization

TikTok

Seattle (WA)

On-site

USD 122,000 - 243,000

Full time

3 days ago

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Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Paid parental leave

Disability coverage (short/long-term)

Life insurance

Wellbeing benefits

10 paid holidays annualy

10 paid sick days annualy

17 days Paid Personal Time

Job summary

TikTok in Seattle, WA is hiring a Machine Learning Engineer Graduate for 2027 start, onsite. You will work on e-Commerce recommendation and marketing algorithms to help users discover products and drive measurable growth.

You will join the TikTok e-Commerce Recommendation and Marketing Algorithm team, building large-scale systems with a focus on personalization, uplift modeling, and operations research to boost GMV and secure shopping experiences.

Qualifications

Bachelor’s degree in Software Development, Computer Science, Computer Engineering, or a related discipline.
Master’s degree in Software Development, Computer Science, Computer Engineering, or a related discipline.
Experience with C++ and Python; Big Data tools (Hive SQL, Spark, MapReduce).
Experience with deep learning tools (TensorFlow or PyTorch).
Strong algorithms and data structures foundation.
Excellent communication and teamwork skills.
Experience with recommendation systems, advertising, or user growth is a plus.

Responsibilities

Help optimize TikTok e-Commerce growth and marketing algorithms, including core capabilities such as user value modelling, personalized messaging, recommendations, and intelligent marketing.
Contribute to establishing a user lifecycle data and value system to address key pain points and business challenges tied to TikTok Mall user growth.
Support implementation of product and technical solutions for user growth engines, including personalized push notifications/emails and recommendation handling to increase e-Commerce DAU and penetration rates.
Optimize recommendation algorithms related to recall/sorting for new user recommendations, improving traffic source handling and the relevance, accuracy, and diversity of new recommendations.
Improve intelligent marketing algorithms using uplift models and operations research methods to increase marketing efficiency and drive e-Commerce GMV growth.