Quant Engineer
1 week ago
Work:
Strategy Development : Utilize Deep Reinforcement Learning (DRL) techniques (such as PPO, DQN) to train high-frequency decision models, addressing real-time optimization problems like dynamic order placement for limit orders and cross-exchange arbitrage.
Data Engine Construction : Design low-latency data pipelines to process multi-modal inputs, including L2/L3 order books, on-chain data, and macro sentiment signals.
Adversarial Training : Build a simulated trading environment to mimic market participant behavior using GANs and Inverse Reinforcement Learning, enhancing strategy robustness.
Hardware Collaborative Optimization : Collaborate with FPGA engineers to deploy AI models in ultra-low-latency trading systems (microsecond-level decision making).
Risk Control : Develop a real-time adaptive risk control module that uses Bayesian deep learning to dynamically adjust position exposure.
Academic Background:
• Top-tier university PhD or Master's in Computer Science/Mathematics/Physics, with research focused on reinforcement learning, multi-agent systems, and time-series forecasting.
Industry Experience:
• Over 2 years of hands-on experience in quantitative funds or high-frequency trading institutions, having led AI strategy development from research to live trading.
• Proficient in core HFT concepts: tick-to-trade latency optimization, order book alpha extraction, and iceberg order detection.
Technical Stack:
• Python/C++, with project code implementing DRL agents using PyTorch/TensorFlow (not Kaggle-style toy projects).
• Familiar with cryptocurrency exchange APIs (e.g., Binance, OKX, Bybit) and WebSocket data stream processing.
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