About the Role

We are seeking a seasoned Equity Quant Trader to lead the development and training of an AI‑driven trading agent for equity markets. In this role, you will bridge quantitative finance, systematic trading, and machine learning – leveraging your deep market expertise to design, backtest, and supervise the behavior of autonomous trading agents.

Key Responsibilities

· Design and implement quantitative trading strategies for cash equities and equity derivatives, with a primary focus on training an AI agent (e.g., reinforcement learning, imitation learning, or LLM‑based trading systems).

· Develop the reward functions, state representations, and action spaces that govern the AI agent’s decision‑making process in live market environments.

· Validate and refine agent behavior through rigorous backtesting, simulation, and live paper trading – ensuring alignment with risk management and performance objectives.

· Analyze market microstructure, order flow, and alternative data to inform feature engineering and agent training.

· Collaborate with AI engineers to integrate the agent into a production trading system, including real‑time risk controls and execution logic.

· Monitor agent performance and intervene or retrain models as market regimes evolve.

Required Qualifications

· 10+ years of hands‑on experience in equity quantitative trading, systematic market making, or high‑/mid‑frequency trading.

· Proven track record of developing and deploying profitable systematic equity strategies (PnL responsibility preferred).

· Strong programming skills in Python (pandas, numpy, scikit‑learn, PyTorch/TensorFlow) and experience with backtesting frameworks.

· Deep understanding of equity market microstructure (e.g., order book dynamics, slippage, fees, liquidity).

· Familiarity with machine learning / reinforcement learning concepts applied to trading – you may not be an ML researcher, but you must be able to design training goals and evaluate agent behavior effectively.

· Excellent risk management mindset and experience with production trading constraints.

· Fluent English and Mandarin

Preferred

· Experience directly training or supervising RL agents for trading (e.g., using stable‑baselines, Ray RLlib, or custom frameworks).

· Background in statistical arbitrage, factor models, or execution algorithms.

· Prior work with alternative data (satellite, sentiment, ESG, etc.) and feature engineering for ML models.

· Experience with low‑latency systems or C++.