Machine Learning Engineer Intern (E-Commerce Recommendation Live) - 2027 Start (PhD)

TikTok is a short-form mobile video platform that inspires creativity and brings joy. The Machine Learning Engineer Intern will develop and optimize recommendation models for live commerce across recall, ranking, multimodal modeling, generative recommendation, and reinforcement learning, partnering with cross-functional teams to launch scalable solutions and deliver measurable business impact.

What you'll do:

• Build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking to improve GMV, conversion, watch time, and long-term user value
• Develop cross-domain and multimodal modeling solutions that connect videos, live streams, products, and user behavior to better power live commerce recommendations
• Advance next-generation recommendation technologies, including generative recommendation, large recommendation models, reinforcement learning, and long-term value optimization
• Partner with cross-functional teams to launch scalable solutions, run experiments, and turn research into measurable business impact

What they're looking for:

• Currently pursuing a PhD in Computer Science, Engineering, Operations Research or a related technical discipline
• Solid foundation in machine learning and at least one of the following areas: recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI
• Strong programming skills in Python or C++, and hands-on experience with deep learning frameworks such as PyTorch
• Good understanding of data structures, algorithms, and large-scale model training or production machine learning systems
• Strong analytical and problem-solving skills, with the ability to translate business problems into effective modeling solutions
• Self-driven and results-oriented, with the ability to take ownership of model iteration and online impact from end to end
• Experience in recommendation systems, especially in live commerce, e-commerce, search, ads, or other large-scale consumer products
• Experience with generative recommendation, large recommendation models, retrieval and ranking systems, or related recommendation architecture upgrades
• Experience with LLMs or multimodal foundation models, including pre-training, post-training, representation learning, contrastive learning, SFT, or RL-based optimization
• Experience in cross-domain transfer learning, LTV modeling, long-term value optimization, causal inference, or debiasing
• Experience with long-sequence user behavior modeling, multi-task learning, multi-interest modeling, or large-scale distributed training and inference optimization
• Publications in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, ACL, CVPR, SIGIR, or RecSys, or strong achievements in major technical competitions

Benefits:

• Interns have day one access to health insurance, life insurance, wellbeing benefits and more.
• Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year).
• Interns who are not working 100% remote may also be eligible for housing allowance.