Responsibilities
Research and implement high-frequency trading strategies, leveraging deep knowledge of market microstructure Analyze large-scale market data to uncover inefficiencies and design robust, data-driven models Build and maintain simulation and backtesting tools aligned with real-world trading conditions Write and optimize production-grade code for signal generation, execution logic, and infrastructureponents Collaborate across disciplines to ensure seamless integration of research and engineering efforts Monitor strategy performance, adapt models to changing market conditions, and manage risk
Requirements
Strong experience in high-frequency trading or systematic strategies within crypto or traditional markets Advanced programming skills in
Python, along with proficiency in at least onepiled language (
Rust preferred, C++ or Go also wee) Deep understanding of market microstructure and the technical nuances of low-latency trading Background in a quantitative discipline such as mathematics, statistics, physics,puter science, or engineering (MSc or PhD preferred) Practical experience working with large datasets, real-time data pipelines, and cloud-based research environments Familiarity with version control systems (Git), Linux/Unix environments, and containerization tools such as Docker Strong problem-solving ability, high attention to detail, and a mindset geared toward continuous improvement
Location
This role is based inLondon. We believe in the power of close collaboration, and candidates should either be located in London or willing to relocate. Support for relocation is available.
Job ID SH
