Source description
About the role
We are seeking a highly skilled Quantitative ML Researcher to join our HFT trading team. Youll be working at the intersection of quantitative research, machine learning, and high-performance software engineering, helping to develop, implement, and optimize trading strategies deployed in global financial markets. This is a hybrid research-engineering role where you'll collaborate with traders, researchers, and infrastructure engineers to create cutting-edge tools and models that drive our trading decisions. Requirements Design, implement, and optimize statistical and ML-based trading models. Develop high-performance, low-latency code in C++, Python, or Rust. Analyse large-scale, high-frequency data to identify predictive signals (alpha). Collaborate with research and trading teams to backtest and deploy strategies in live environments. Build tools for feature engineering, data normalisation, and model evaluation. Improve execution algorithms for minimising slippage, market impact, and latency. Contribute to building robust infrastructure for automated model training and deployment. We are seeking a highly skilled Quantitative ML Researcher to join our HFT trading team. Youll be working at the intersection of quantitative research, machine learning, and high-performance software engineering, helping to develop, implement, and optimize trading strategies deployed in global financial markets. This is a hybrid research-engineering role where you'll collaborate with traders, researchers, and infrastructure engineers to create cutting-edge tools and models that drive our trading decisions. Requirements Design, implement, and optimize statistical and ML-based trading models. Develop high-performance, low-latency code in C++, Python, or Rust. Analyse large-scale, high-frequency data to identify predictive signals (alpha). Collaborate with research and trading teams to backtest and deploy strategies in live environments. Build tools for feature engineering, data normalisation, and model evaluation. Improve execution algorithms for minimising slippage, market impact, and latency. Contribute to building robust infrastructure for automated model training and deployment.