Machine Learning Engineer Intern (Commerce Ads) - 2025 Summer (BS/MS)

hs. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. It runs for 12 weeks beginning in May/June 2025 . Successful candidates must be able to commit to one of the following summer internship start dates below:

• Monday, May 12
• Monday, May 19
• Tuesday May 27 (Memorial Day May 26)
• Monday, June 9
• Monday, June 23

Please state your availability clearly in your resume (Start date, End date).

Key Responsibilities
• Build highly scalable machine learning systems and state-of-the-art machine learning models to improve ads ranking quality and optimize advertisers' marketing strategies. Examples include but are not limited to click through rate prediction, conversion rate prediction, intelligent format selection and user journey optimization.
• Explore, develop and experiment with new features to improve model accuracy.
• Understand ads platform objectives and take full advantage of modern machine learning to improve ads relevance, quality, and quantity delivered to end-users.
• Collaborate with Product Managers, Designers, and other disciplines to explore the next generation of shopping experiences on TikTok.

Qualifications

Minimum Qualifications
• Pursuing a BS/MS degree in Computer Science, Computer Engineering, or other relevant majors, with related work experience.
• Solid programming skills, including but not limited to: Go, C/C++, Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
• Good analytical thinking capability. Have essential knowledge and skills in statistics.
• Good theoretical grounding in the machine and deep learning concepts and techniques (CNN/RNN/LSTM, etc.).
• Familiar with the architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/PyTorch/MXNet), familiar with its architecture and implementation mechanism.

Preferred Qualification
• Good understanding in one of the following domains: ads bidding & auction, ads quality control, and online advertising systems (familiar with one or more of these terms: CPC/CPM, CTR/CVR, Ranking /Targeting, Conversion/Budget, Campaign/Creative, Demand/Inventory, DSP/RTB).
• Experience in resource management and task scheduling with large-scale distributed software (such as Spark and TensorFlow).
• Relevant work or research experiences in search and recommendation.