Software Engineer - Research Infrastructure

Our cash equities engineering team is building out systems that must ingest and react to market data feeds from major US exchanges in microseconds or less, efficiently price and hedge positions of single stocks and ETFs and execute orders with extremely low error tolerance. We continually refine, store, and analyze terabytes of data produced by market activity and our trading systems. Our user interfaces must present coherent, responsive real-time visualizations of market activity and system performance while providing intuitive control of a highly complex system. Our core systems must provide a stable, performant, and trusted foundation on which our systems are built You'll build the Quantitative Research framework at the core of our systematic equities business. The framework models how trading strategies would behave against historical market data, allowing researchers to test ideas before they ever reach production. The same framework researchers use to develop and test new trading ideas is the one that powers production, meaning the systems you build have a direct impact on what the desk can research, deploy and trade. Working alongside quant researchers and traders, you'll develop the tools, datasets and distributed systems that enable large-scale research across the cash equities markets in US, Europe and APAC. Every improvement you make helps the team evaluate new ideas and deploy them into production faster. What you'll do You'll work closely with researchers and traders to build the software that underpins our systematic trading platform, from research through to production. Your work will include: • Developing and extending the simulation framework used to research and run systematic cash equities strategies across ETFs and stocks. • Building distributed systems that scale backtesting and model training across cloud and on-premise compute environments. • Curating high-quality research datasets by combining live and historical market data into accurate, reproducible and scalable inputs for large-scale experimentation. • Working directly with researchers to understand new requirements and translate them into robust, production-ready software. • Improving the tools and workflows that allow researchers to experiment, validate ideas and deploy strategies efficiently.