AI Engineer Intern

Established in March 2018, Bybit is one of the fastest growing cryptocurrency derivatives exchanges, with more than 70 million registered users. We offer a professional platform where crypto traders can find an ultra-fast matching engine, excellent customer service and multilingual community support. We provide innovative online spot and derivatives trading services, mining and staking products, as well as API support, to retail and institutional clients around the world, and strive to be the most reliable exchange for the emerging digital asset class.

Our core values define us. We listen, care, and improve to create a faster, fairer, and more humane trading environment for our users. Our innovative, highly advanced, user-friendly platform has been designed from the ground-up using best-in-class infrastructure to provide our users with the industry's safest, fastest, fairest, and most transparent trading experience. Built on customer-centric values, we endeavour to provide a professional, 24/7 multi-language customer support to help in a timely manner.

As of today, Bybit is one of the most trusted, reliable, and transparent cryptocurrency derivatives platforms in the space.

Key Responsibilities

• LLM Application Development: Participate in the development of LLM-based applications, including Q&A systems, Agents, and algorithmic workflow systems.
• Workflow & Prompt Engineering: Assist in workflow development, as well as Prompt design and optimization to improve model performance.
• Agent Capabilities: Assist in implementing Tool Use (Function Calling) and foundational multi-turn dialogue logic.
• Core NLP Tasks: Implement tasks such as text classification, information extraction, and semantic matching.
• Model Modeling: Participate in small-scale modeling (classification/labeling tasks) and performance optimization.
• Data Management: Perform data cleaning, basic feature engineering, and result analysis.

Job Requirements

Basic Requirements

• Coding Proficiency: Proficient in Python with good coding habits and strong problem-solving skills.
• NLP Fundamentals: Familiar with common NLP tasks (Text Classification, NER, Semantic Matching, etc.) and their standard methodologies.
• Modeling Experience: Capable of independently completing the training and fine-tuning of simple classification or labeling models.
• Theoretical Knowledge: Deep understanding of Transformer / BERT principles (structure, training objectives, and application scenarios).
• LLM Exposure: Experience with Large Language Models (e.g., API calls, basic Prompt Engineering).
• Agent Concepts: Understanding of Agent fundamentals (e.g., Tool Use, multi-turn dialogue) or experience with basic implementations.

Preferred Qualifications (Plus)

• Practical Experience: Hands-on experience with Q&A systems (QA / Chatbot) or RAG (Retrieval-Augmented Generation) frameworks.
• ML/DL Foundations: Solid understanding of Machine Learning/Deep Learning (training workflows, loss functions, etc.).
• Software Engineering: Proficiency in backend development (API design and service implementation).
• Project Portfolio: Demonstrated experience through course projects, personal side projects, or open-source contributions.