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ML Engineer Graduate (TikTok E-commerce Recommendation) - 2025 Start (PhD)

TikTok
San Jose
Full-time

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy.

TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join UsCreation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.

Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day. To us, every challenge, no matter how difficult, is an opportunity;

to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always. At TikTok, we create together and grow together.

That's how we drive impact - for ourselves, our company, and the communities we serve. Join us. Interest-based E-commerce is a new and fast growing business that aims at connecting all customers' interests to excellent sellers and high quality products on TikTok Shop.

Different from other traditional E-commerce platforms, Tiktok Shop provides customers with personalized and unique shopping experience through E-commerce live-streaming and E-commerce short videos.

The recommendation system plays an extremely important role in helping customers explore their shopping interests. We are a group of applied machine learning engineers and research scientists that focus on E-commerce video / live-streaming recommendations on the major traffic source of Tiktok ForU page, where we serve traffic for billions of users every single day.

Our goal is to connect users with products they love, creating a seamless and engaging shopping journey. We utilize cutting-edge machine learning techniques to provide personalized product / video / live recommendations, boosting user satisfaction and driving e-commerce success.

We are looking for talented research scientists to join our team in 2025, you will be instrumental in designing, implementing, and optimizing machine learning models that power our recommendation systems.

You will collaborate with a team of skilled engineers and researchers to build scalable solutions that enhance the e-commerce experience for TikTok users.

This role offers the chance to apply your research expertise to solve real-world problems and make a substantial impact on our growing e-commerce platform.

Responsibilities 1. Develop, implement, and optimize machine learning models for personalized product / video / live recommendations.

2. Collaborate with cross-functional teams to gather requirements and integrate machine learning solutions into the e-commerce platform.

3. Conduct data analysis to uncover insights and drive model improvements.4. Research and experiment with cutting-edge machine learning techniques, including NLP, computer vision, and information retrieval, to enhance recommendation accuracy and user satisfaction.

5. Evaluate and improve the performance of existing recommendation algorithms.

Minimum Qualifications : 1. PhD or equivalent degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.

2. Strong understanding of machine learning algorithms and techniques,3. Proficiency in programming languages such as Python, Java, or C++, Solid background in data analysis and statistical modeling.

4. Familiar with machine learning frameworks and libraries (., TensorFlow, PyTorch, Scikit-learn).5. Excellent problem-solving skills and the ability to think creatively and critically.

Ability to work effectively in a collaborative team environment. Preferred Qualifications : 1. Strong first-author publications in top-tier machine learning, NLP, computer vision, or information retrieval conferences / journals (.

NeurIPS, ICML, CVPR, ACL, SIGIR, KDD, EMNLP).2. Familiarity with deep learning techniques and neural network architectures.

3. Experience in e-commerce or related AI research domains.

30+ days ago
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