Job Responsibilities: • Efficiently and reliably implement and deploy high-frequency quantitative researchers’ strategy logic into the live trading environment • Interface with exchange, broker, or internal matching system APIs to enable high-performance integration between strategies and order execution systems • Continuously optimize core performance metrics such as execution latency, memory usage, and order processing efficiency • Develop or enhance high-frequency strategy simulation/backtesting frameworks supporting simulated trading with real market data (tick/level 2) • Build strategy-level monitoring, logging, and anomaly-detection systems to ensure stable operation • Collaborate with quantitative researchers, traders, and system engineers to drive the end-to-end process from strategy R&D to live deployment Qualifications: • Bachelor’s degree or above, preferably in Computer Science, Electronic Engineering, Automation, Financial Engineering, Mathematics, Physics, or related fields • Proficient in C++, Java, Rust, Go, or C, with strong system-design and high-performance programming skills • Solid understanding of high-frequency trading strategy mechanics, including execution logic, state management, and risk-control triggers • Proven practice in system stability, fault tolerance, logging, and anomaly monitoring • Excellent communication skills to work effectively with researchers and traders, quickly understanding and implementing strategy requirements Preferred Qualifications: • Hands-on experience developing live high-frequency trading systems • Familiarity with matching engines and API interfaces for A-shares, Hong Kong stocks, futures, or cryptocurrency markets • Experience with high-frequency matching, risk-control modules, and order-book modeling • Track record of building a live trading strategy framework from the ground up