Model Development & Experimentation Under guidance, participate in the research, reproduction, training, tuning, and evaluation of machine learning and deep learning models to solve real-world business problems
Data Processing & Analysis Collect, clean, label, and preprocess data perform feature engineering to build reliable datasets and extract insights
Engineering Implementation & Support Convert experimental models into reproducible and maintainable code assist with model deployment, A/B testing, and performance monitoring in production
Technology Research & Knowledge Sharing Stay up to date with the latest advancements in AI participate in internal knowledge sharing and explore applying cutting-edge techniques to real-world scenarios
Documentation & Collaboration Clearly document experiments, results, and technical solutions collaborate effectively with cross-functional teams including algorithm, backend, and product
Qualifications
Education
Bachelor's degree or above in Computer Science, Artificial Intelligence, Software Engineering, Mathematics, Statistics, or related fields
2025/2026 graduates are preferred
Fundamental Knowledge
Solid programming skills, with proficiency in Python
Strong foundation in data structures, algorithms, and software engineering
Understanding of machine learning and deep learning fundamentals, including common models and algorithms
Technical Skills
Familiarity with at least one major deep learning framework (PyTorch or TensorFlow), with hands-on experience through coursework, projects, competitions, or internships
Strong data analysis skills, with experience using libraries such as pandas, NumPy, and scikit-learn
Soft Skills
Strong curiosity and intrinsic motivation, with a genuine passion for AI
Excellent problem-solving and analytical skills, with the ability to break down complex problems
Good communication and teamwork skills, with the ability to clearly express ideas
Results-oriented mindset, with the ability to perform in a fast-paced, high-expectation environment
Preferred Qualifications
Strong performance in well-known AI competitions (e.g., Kaggle, Tianchi)
High-quality technical projects on GitHub or contributions to open-source projects
Publications or patents in relevant fields
Hands-on experience with LLM-related technologies (e.g., LangChain, RAG, fine-tuning)
In-depth knowledge in specific domains such as computer vision (CV), NLP, multimodal learning, or reinforcement learning
