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

Our E-commerce Recommendation Team is responsible for building and scaling our recommendation system to provide the best shopping experience for TikTok users. We are looking for talented PhD interns to actively contribute to our products and research, shaping the organization's future plans and emerging technologies. This dynamic internship blends hands-on learning, community-building, professional development, and collaboration with industry experts.

Responsibilities

• Generative Recommendation Research: Drive the evolution of recommender systems from discriminative to generative paradigms; explore frontier directions such as generative retrieval and generative re-ranking/blending; improve personalization while optimizing training and inference efficiency of generative models on GPUs.
• LLM for Recommendation Research: Leverage Large Language Models (LLMs), Reinforcement Learning (RL), and related techniques to enhance semantic understanding and reasoning in recommender systems, addressing challenges like cold start, long-tail item distribution, and user intent understanding.
• Agentic Recommendation Research: Explore self-evolving agents to continuously optimize recommender systems, evolving them toward agentic architectures capable of real-time, personalized decision-making.
• Long-Term Value and User Experience Modeling: Replace traditional heuristic rule-based systems with LLM and agent capabilities; build algorithms for measuring and optimizing long-term value (LTV) and user experience to enable sustainable platform growth.

Requirements

Minimum Qualifications

• Currently pursuing a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related discipline.
• Solid foundation in machine learning and deep learning, with research or project experience in at least one of the following: large language models, reinforcement learning, generative models, recommender systems, or information retrieval.
• Proficient in Python and at least one mainstream deep learning framework (e.g., PyTorch, TensorFlow, JAX).
• Strong problem-solving skills and passion for tackling complex, open-ended research problems.

Preferred Qualifications

• 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 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.
• Strong curiosity about new technologies, fast learning ability, and a passion for solving challenging real-world problems.

Pay

The hourly rate for this internship position is $57.

Benefits

• Day-one access to health insurance, life insurance, and wellbeing benefits.
• 10 paid holidays per year and paid sick time (56 hours if hired in the first half of the year, 40 if hired in the second half).
• Eligibility for housing allowance if not working 100% remotely.

About the Company

TikTok is the leading destination for short-form mobile video, with a mission to inspire creativity and bring joy. Our global headquarters are in Los Angeles and Singapore, with additional offices in cities like New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. We foster an inclusive environment where diverse voices are celebrated, and innovation thrives. Our team is driven by curiosity, humility, and a desire to make an impact in a rapidly growing tech company.