Responsibilities ● Mine alpha factors and build predictive models via deep learning based on multi-dimensional financial market data. ● Explore signal fusion and strategy ensemble approaches to enhance model robustness and portfolio return characteristics. ● Rapidly prototype, reproduce and optimize state-of-the-art deep learning models with mainstream ML frameworks. ● Stay updated on latest academic and industrial research, conduct ongoing model iteration and performance enhancement. Requirements ● Bachelor’s degree or above from top domestic and international universities, majoring in Computer Science, Mathematics, Statistics, Machine Learning or related quantitative disciplines. ● Strong theoretical foundation in machine learning, proficient in Python and mainstream deep learning frameworks; capable of end-to-end data processing and independent modeling. ● Hands-on research or project experience in time series forecasting, NLP or other deep learning related domains. ● Logical, rigorous mindset with excellent self-learning capability and strong interest in applying ML to quantitative finance. ● Prior internship or working experience in Internet, AI, fintech or quantitative domains. Preferred Qualifications ● Kaggle competition awards or first-author publications at top ML conferences (NeurIPS / ICML / ICLR). ● Relevant internship experience in quantitative trading, asset management or financial technology.