Performance Engineer

Performance Engineer - Market Microstructure & Low-Latency Systems Location: London / Zurich We are partnered with a leading global quantitative trading group known for pushing the boundaries of ultra-low-latency engineering. They are looking for a Performance Engineer specialising in market microstructure, exchange connectivity and network-level optimisation to drive performance improvements across some of the most competitive trading environments in the world. Your work will directly shape how strategies interact with global exchanges, impacting latency, fill rates and trading performance. If you enjoy reverse-engineering exchange behaviour, analysing packet captures at nanosecond resolution, tuning Linux until it screams, or uncovering microstructure patterns that move PnL - you will thrive here. Key Responsibilities Analyse network traffic, exchange protocol behaviour and wire-level message flows to uncover inefficiencies Reverse-engineer market-microstructure features and

exchange matching behaviour to improve execution quality Research exchange connectivity, attend technical briefings and integrate insights into trading operations Design and run controlled experiments to optimise fill rates, order routing and execution behaviour Collaborate closely with quant, trading and engineering teams to translate findings into measurable PnL impact Build tooling, automation and performance-monitoring systems over high-volume, high-resolution datasets Key Skills & Experience 5+ years in low-level systems engineering, performance engineering, network engineering, or execution-side trading infrastructure Strong knowledge of Python for analysis and tooling, including time-series analysis Solid understanding of TCP/IP, Ethernet, packet capture analysis, protocol debugging Experience with Linux performance tuning, kernel-bypass networking or lock-free concurrency is highly desirable Exposure to market microstructure, direct market access, FIX/ITCH/OUCH, or exchange protocols is a strong advantage Ability to work hands-on with large, high-fidelity datasets and extract signal from noise Curiosity, deep technical ownership, and a drive to optimise systems at the microsecond and sub-microsecond level