Join the E-commerce Global Supply Chain and Logistics team at TikTok. We are building AI-native capabilities for global logistics, including logistics agents, address intelligence, context engineering, agent evaluation, and workflow automation for complex supply chain operations. This role is for candidates who want to apply LLMs, agents, reinforcement learning, retrieval systems, and software engineering to real logistics problems at global scale.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities:
- Responsible for the development of deep learning and operations research models and related intelligent systems for the supply chain and logistics of the global E-Commerce business.
- Utilize e-commerce big data and deep learning models to predict end-to-end estimated time of arrival (ETA), and some logistics events such as failed delivery, delivered but not received to enhance the user logistics experience. Build logistics network knowledge graphs and predict the spatio-temporal trajectory sequence of express packages through deep learning, statistical inference and other algorithmic methods. Use NLP and LLM algorithms to handle address problems such as address verification and address suggestion.
- Utilize time series forecasting techniques to predict sales at different granularities and horizons, such as warehouse-level manpower forecasting, inventory-level demand forecasting etc. We need strong machine learning and deep learning skills to detect important factors and model the relationship between the future and history.
- Participate in the growth of e-commerce merchants/creators, responsible for the growth algorithm of e-commerce merchants/creators, including potential merchants/creators mining algorithm, tiering algorithm, out-reach algorithm, growth algorithm, etc. Work closely with e-commerce merchants/creators related operations and product teams to achieve the high-quality growth goal of e-commerce merchants/creators.
- Combined with massive e-commerce information, through data mining and machine learning, predict the performance of products in traffic/conversion, improve the efficiency of product operation strategy and pricing/subsidy strategy, and assist in optimizing traffic distribution, to satisfy the requirements of product growth, cost optimization and some other scenarios.
Minimum Qualifications:
- Individuals who are completing or have recently completed a Bachelor's degree in artificial intelligence, computer science, operations research, automation, statistics, mathematics, or a related discipline
- Proficient in machine learning and statistics, familiar with the principles and model structures of common machine learning and deep learning algorithms for classification, regression, and clustering, with practical application experience.
- Proficient in programming languages such as Python/Java, possessing excellent coding abilities, and familiar with at least one common machine learning/deep learning framework (TensorFlow/PyTorch etc.).
- Demonstrates a spirit of exploration, outstanding data analysis capabilities, strong initiative, and excellent teamwork and communication skills.
Preferred Qualifications:
- Individuals who are completing or have recently completed a Master's degree in Software Development, Computer Science, Computer Engineering or a related discipline.
- Hands-on experience with coding agents, long-horizon agents, computer-use agents, agent harnesses, workflow orchestration, or automated evaluation frameworks.
- Experience with LLM post-training, including SFT, DPO, PPO, GRPO, RLHF, RLAIF, reward modeling, counterfactual data, or evidence-driven decision training.
- Experience building benchmarks from real production questions, including taxonomy design, golden answer construction, error attribution, and regression evaluation.
- Experience with logistics, e-commerce, operations research, data platforms, knowledge graphs, or enterprise knowledge management systems.
- Published papers or strong open-source work in LLMs, agents, NLP, data mining, machine learning systems, evaluation, or AI engineering.