机器学习工程师实习生（电商供应链与物流-LLM）

Team Introduction: 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 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: - Responsible for supporting the development of large language models, agent systems, and related intelligent systems for the supply chain and logistics of the global E-Commerce business. - Participate in building domain LLM model capabilities for e-commerce supply chain and logistics, including continued pre-training / CPT, SFT, preference optimization, reinforcement learning such as GRPO / PPO, reward or judge model design, model compression, inference cost and latency optimization, and landing scenarios such as address correction, trajectory prediction, logistics cost analysis, customer service semantic understanding, and root-cause analysis. - Support multimodal and structured understanding capabilities for logistics and supply chain scenarios, focusing on unified modeling of text, numerical time series, events, product attributes, images, and documents to support scenario simulation, explainable prediction, and integration with existing forecasting or decision systems. - Support core agent capability development for team and business workflows, including AutoResearch, Harness-based task decomposition and execution loops, RAG and knowledge retrieval, context understanding, skill / tool use, evidence grounding, and Clone & Adapt workflows for reusing proven solutions across markets and logistics scenarios. - Support agent architecture, engineering, evaluation, and evolution work, including runtime orchestration, memory and state management, model / tool routing, permission-safe execution, observability, benchmark and Golden Set evaluation, badcase attribution, regression testing, online feedback loops, and continuous improvement of context, skills, workflows, and model behavior.