NLP Algorithm Engineer Intern

About Us

Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance.

Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.

Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services.

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 Ge