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

TikTok is hiring a Machine Learning Engineer Intern - E-Commerce Recommendation Mall (2027 Start) in San Jose, CA (remote). When: Summer 2027. Pay stated by the employer: $60/hr. Posted November 12, 2026. Checked against the employer's posting on November 12, 2026.

Responsibilities Our E-commerce Recommendation Team is responsible for building up and scaling our recommendation system to provide the best shopping experience for our TikTok users.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.

Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.

Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.

Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

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; continuously improve personalization capabilities while deeply optimizing the 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 the semantic understanding and reasoning capabilities of recommender systems, addressing core business challenges in e-commerce scenarios (e.

g., cold start, long-tail item distribution, and user intent understanding). - Agentic Recommendation Research: Explore the construction of self-evolving agents and leverage agents to continuously optimize recommender systems; drive the evolution of recommender systems toward agentic architectures capable of keenly perceiving user context and making real-time, personalized decisions and adjustments.

- Long-Term Value and User Experience Modeling: Explore replacing traditional heuristic rule-based systems with LLM and agent capabilities; build next-generation algorithms for measuring and optimizing long-term value (LTV) and user experience, enabling sustainable growth of the platform ecosystem.

Qualifications Minimum Qualifications: - Currently pursuing a Undergraduate degree in Software Development, Computer Science, Computer Engineering or a related discipline.

- Solid foundation in machine learning and deep learning, with research or project experience in at least one of the following areas: 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 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.

- Strong curiosity about new technologies, fast learning ability, and a passion for solving challenging real-world problems. Job Information 【For Pay Transparency】Compensation Description (Hourly) - Campus Intern The hourly rate range for this position in the selected city is $45- $45.

Benefits may vary depending on the nature of employment and the country work location. 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.

The Company reserves the right to modify or change these benefits programs at any time, with or without notice. For Los Angeles County (unincorporated) Candidates: Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1.

Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues; 2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and 3.

Exercising sound judgment. About TikTok 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 we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.