ML Researcher/Engineer

About the role

A 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 Responsibilities

• Research, 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 Qualifications

• Bachelor'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 Qualifications

• Experience with reinforcement learning, deep learning for time series, or NLP.
• Exposure to low-latency systems, distributed computing, or GPU-accelerated workflows.
• Publications or open-source contributions in ML or quantitative finance.