Machine Learning Engineer Intern (Commercial AI-CRM and Transaction) - 2027 Summer

Responsibilities

The Commercial AI-CRM and Transaction team focuses on TikTok advertiser growth algorithms. Leveraging deep learning and large language model technologies, the team builds an algorithmic system for advertiser growth, optimizes the operational efficiency of global commercial platforms, and empowers sustainable long-term business revenue growth.

The team is exploring a new paradigm of enterprise intelligence in the era of large models, building next-generation intelligent Agent systems for customer growth, sales operations, and business decision-making. The team is committed to the innovative application of LLM, Agent, multimodal intelligence, and machine learning technologies in real-world business scenarios, driving the evolution of AI from an "assistant tool" into an intelligent partner capable of understanding business, developing strategies, and executing tasks.

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:
- Explore Enterprise-level Intelligent Agent Technologies: Research LLM Agent technologies, including complex reasoning, task planning, knowledge augmentation, long-term memory, and autonomous decision-making. Build intelligent systems capable of understanding business objectives, completing complex tasks, and continuously optimizing.
- Intelligent Dialogue and AI Call Technologies: Explore next-generation intelligent interaction technologies, including real-time speech, multi-turn dialogue, intent understanding, and dialogue strategy generation. Build AI Sales Agents with natural communication capabilities to improve customer engagement and conversion efficiency.
- Intelligent Decision-Making and Risk Management: Combine customer data, business knowledge, and machine learning models to explore customer risk identification, credit limit estimation, and intelligent decision-making capabilities. Help businesses achieve a balance between growth and risk management.
- Intelligent Customer Understanding and Business Insights: Leverage LLM and machine learning technologies to understand customer industries, brands, business status, and organizational relationships. Build intelligent customer profiles, identify customer needs and potential business opportunities, and enhance customer management capabilities.

Qualifications

Minimum Qualifications:
- Currently pursuing a Master's degree in Computer Science, Artificial Intelligence, Natural Language Processing, or a related technical discipline.
- Familiarity with at least one mainstream Agent framework (e.g., LangGraph, OpenClaw, Hermes, Codex, Claude Code, etc.) with hands-on experience.
- Deep understanding of the core principles of Large Language Models (LLMs) and AI Agents. Familiarity with mainstream Agent architectures (e.g., ReAct/PlanAct), Multi-Agent systems, and concepts such as Context Engineering and Memory.
- Solid AI/ML background with in-depth knowledge of post-training techniques such as SFT and RLHF.
- Excellent coding skills with strong fundamentals in data structures and algorithms. Proficiency in C/C++ or Python.
- Strong teamwork, collaboration, and communication skills.

Preferred Qualifications:
- Practical experience in fine-tuning model capabilities for specific tasks is preferred.