Develop and maintain high-frequency trading systems, implement optimized production code from quantitative prototypes, and contribute to data pipelines, simulators, backtesting, and monitoring software. Interns may rotate across quantitative research and development tracks involving statistical arbitrage and market-making strategies for commodities and digital assets. The role includes collaboration with infrastructure teams, quants, and traders, plus coding assessments and quantitative interviews.

Conduct quantitative research for high-frequency trading and prediction-market strategies. Analyze order book, trade, sports, and prediction-market data; develop statistically significant signals, pricing models, liquidity-management techniques, and latency-sensitive strategies. Interns may rotate across trading tracks and participate in coding tests and quantitative interviews. The role requires advanced statistical research, Python, C++, market microstructure knowledge, and machine-learning experience.

Own and extend a C++ simulation and backtesting framework for digital assets and FX. Model exchange matching engines, order queues, and special order types; build pybind11 integrations and Python research tooling; process large historical datasets on NFS; and align simulated results with live trading. Collaborate with traders, researchers, and infrastructure teams while debugging discrepancies and improving research platform reliability.