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💻 About the Role

As a Machine Learning Engineer, you will be involved in building an event warehouse system and optimizing market heat prediction, liquidity management, and user recommendation systems using machine learning techniques. This role focuses on building and optimizing models based on trading data, social media trends, news reports, etc., to improve user experience and optimize market operations.

🗃️ Responsibilities

Event Heat Tracking
- Build an event heat scoring system to measure market activity and predict future trends.
- Use time series models (such as LSTM, Transformer), combining trading data, social media, news reports, etc., to predict market heat.
Liquidity Management
- Optimize liquidity adjustment strategies using Reinforcement Learning (RL) algorithms (such as DQN, PPO) to improve market capital utilization efficiency.
User Recommendation System
- Build personalized recommendation systems based on user transaction behavior, browsing data, and social connections to recommend the most relevant markets.
- Use NLP and LLM (such as GPT, BERT) technologies to match news trends with market events for accurate recommendations.
Event Matching
- Use NLP technologies and Google Trends analysis to intelligently match news hotspots with market events for event recommendations.

🎒 Requirements

A Master's or PhD in Computer Science, Mathematics, Statistics, or a related field.
Strong theoretical foundation in machine learning, deep learning, and natural language processing (NLP), particularly in event prediction and recommendation systems.
Proficiency in at least one programming language, such as Python, C++, or Java, with solid algorithm and data structure skills.
Familiarity with popular machine learning frameworks and tools such as TensorFlow, PyTorch, Keras, etc.
Experience with time series analysis models (such as LSTM, Transformer).
Knowledge of reinforcement learning (DQN, PPO) and its application in liquidity management.
Experience in developing recommendation systems, familiar with collaborative filtering (CF), graph neural networks (GNN), BERT, and other recommendation algorithms.
Strong innovation mindset, able to propose and implement new solutions based on cutting-edge research and technology.

➕ Preferred Qualifications

Experience working with large datasets, with knowledge of distributed computing and big data processing tools (such as Spark, Hadoop).
Experience applying NLP and LLM technologies (such as GPT, BERT) in real-world business applications.
Published academic papers or participation in major academic conferences in related fields.

🎁 Salary Range

📌 Full-time position

Responsibilities:
- Responsible for algorithm optimization and iteration of recommendation system.
- Collaborate with the product team to design and implement personalized recommendation solutions.
- Regularly evaluate and improve the algorithm effect.
Requirements:
- Master degree or above, majoring in Computer Science, Mathematics or related fields.
- 3+ years of experience in machine learning or recommendation systems.
- Proficient in Python / Java, TensorFlow, PyTorch and other frameworks.
Salary: 35k – 50k CNY * 12 + performance bonus.

📌 Internship Positions

Responsibilities:
- Assist the team in algorithm experimentation and data analysis.
- Complete the basic development and tuning of algorithm modules.
- Participate in the experimental platform construction and maintenance of the recommendation system.
Requirements:
- Bachelor or graduate student, majoring in Computer Science, Mathematics or related majors.
- Proficient in Python, experience in machine learning or deep learning projects is preferred.
Salary: 500 – 1,000 CNY/day.