ML Researcher/Engineer

Machine Learning Engineer (Research-Focused) - Equities TradingQuantitative Research & Trading Technology About the RoleA proprietary trading firm is looking to bring on a Machine Learning Engineer with a strong research orientation to join their expanding equities trading team. This is a hybrid role sitting at the intersection of applied ML research and production engineering - you'll design, prototype, and deploy models that drive alpha generation, execution, and signal research across US and global equities markets. Key ResponsibilitiesResearch, prototype, and productionize ML/statistical models for alpha signals, market microstructure, and execution strategies in equities.Partner with quantitative researchers and traders to translate research ideas into robust, low-latency production systems.Build and maintain scalable data pipelines, feature stores, and backtesting frameworks for large-scale market and alternative data.Explore modern ML techniques (deep learning, reinforcement learning, time-series modeling, NLP on alt-data) and evaluate their applicability to trading problems.Own the full model lifecycle: research → backtest → deployment → monitoring → iteration.Contribute to internal research infrastructure and tooling that accelerates the broader quant/ML team. Required QualificationsBachelor's, Master's, or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or a related quantitative field.3+ years of experience building and deploying ML models in a research-driven or production environment (trading, big tech, or top-tier research labs).Strong proficiency in Python (NumPy, pandas, PyTorch/TensorFlow, scikit-learn); working knowledge of C++ is a strong plus.Solid foundation in statistics, probability, time-series analysis, and modern ML methods.Experience working with large, noisy datasets and building reproducible research pipelines.Strong software engineering fundamentals Preferred QualificationsExperience with reinforcement learning, deep learning for time series, or NLPExposure to low-latency systems, distributed computing, or GPU-accelerated workflowsPublications or open-source contributions in ML or quantitative finance