Machine Learning Engineer Intern (E-Commerce Supply Chain & Logistics-LLM/Agent) - 2027 Start (PhD)

岗位职责 / Responsibilities

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. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.

Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).

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.

任职要求 / Requirements

Minimum Qualifications:

- Currently pursuing a PhD degree in artificial intelligence, computer science, operations research, automation, statistics, mathematics, or a related discipline

- Experience with LLMs, agents, RAG, tool use, post-training, evaluation, or applied NLP systems.

- Strong programming ability in Python and familiarity with at least one production-oriented language such as Java, C++, Go, or TypeScript.

- Familiarity with machine learning and deep learning frameworks such as PyTorch, TensorFlow, JAX, vLLM, Hugging Face, LangChain, LlamaIndex, or similar ecosystems.

Preferred Qualifications:

- Research or project experience in coding agents, long-horizon agents, computer-use agents, agent harnesses, workflow orchestration, or automated evaluation systems.

- Experience with LLM post-training, including SFT, DPO, PPO, GRPO, RLHF, RLAIF, reward modeling, counterfactual data, or evidence-driven decision training.

- Experience building benchmarks or evaluation datasets from real user questions, including taxonomy design, golden answers, error attribution, and regression testing.

- Experience with e-commerce, logistics, operations research, data platforms, knowledge graphs, or enterprise knowledge management systems.

- Published papers, strong open-source contributions, or hands-on projects in LLMs, agents, NLP, data mining, machine learning systems, evaluation, or AI engineering.