AI Engineer (Finance Industry)

Responsibilities:

• AI / ML Model R&D and Alpha Factor Mining:

  • Apply Machine Learning (e.g., XGBoost, LightGBM) and Deep Learning models (e.g., Transformer, LSTM, GNN) to high-dimensional, low SNR financial data to discover robust, explainable Alpha signals.

  • Conduct feature engineering and selection across multi-source financial datasets (Tick/Bar market data, fundamentals, unstructured text/sentiment data).

• Quant Pipeline & Trading System Integration:

  • Design, build, and optimize high-throughput, low-latency offline training and real-time online inference pipelines.

  • Collaborate closely with Quant team to translate AI models into production trading strategies with rigorous backtesting, stress testing, and risk validation.

• Explore Cutting-Edge Quant AI Techniques:

  • Track state-of-the-art ML/DL research in quantitative finance (e.g., execution optimization, portfolio management, LLM/Agent applications in financial analytics) and quickly implement prototypes for Alpha validation.

Requirements:

• Master’s or Ph.D. in Computer Science, Data Science, Financial Engineering, Statistics, Physics, or a related quantitative field preferred.

• Expert in Python and its AI/Data Science ecosystem (PyTorch / TensorFlow, Pandas, NumPy, Scikit-learn). Proficient with Linux environments and Git workflows.

• Finance + AI Hands-on Experience:

  • 3+ years of hands-on AI/ML development or research experience within quantitative hedge funds, proprietary trading firms, investment banks, or top FinTech companies.

  • Quantitative Mindset: Deep understanding of financial time-series data characteristics; strong awareness and solutions for common pitfalls like look-ahead bias, overfitting, and purged/embargo cross-validation.

• Mathematical & Statistical Foundation: Solid knowledge of probability, statistics, linear algebra, and optimization theory, with strong numerical intuition and logical reasoning.

Nice-to-haves:

• Quant Track Record: Demonstrated experience running live trading strategies or high rankings in quantitative competitions (e.g., WorldQuant Challenge, Kaggle financial tracks, QuantConnect competitions).

• High-Performance Engineering: Proficiency in C++ or experience with GPU acceleration (CUDA, TensorRT) for high-frequency or ultra-low-latency real-time inference optimization.

• Frontier AI Applications: Hands-on experience with financial LLMs, RAG frameworks, or Reinforcement Learning (RL) applied to execution or portfolio optimization.