Design, train, and deploy ML models (deep learning, NLP, reinforcement learning, or large-scale ensemble methods) for alpha generation across equities, futures, and other liquid markets
Own the full pipeline from research and backtesting to live production deployment
Mine large, noisy, non-stationary financial datasets to engineer features and signals that hold up out-of-sample
Collaborate with quant researchers, engineers, and traders to integrate ML-driven signals into existing systematic strategies
Ideal Candidate
2 to 5 years of experience applying machine learning in a quantitative research, trading, or comparable high-stakes production environment
Strong foundations in statistics, applied ML, and time-series/panel data methods; healthy skepticism about overfitting in noisy financial data
Proficiency in Python; experience with large-scale data pipelines and model deployment infrastructure
PhD or 2 years of industry experience in ML, statistics, CS, or a related quantitative field a strong plus
Our client is a profitable, fast-growing stealth quantitative trading firm built on a clean-slate architecture that fuses deep market-making expertise...
We’re partnering with a rapidly scaling AI company developing intelligent systems that automate sophisticated, high-impact workflows for enterprise or...
