Machine Leaming Engineer - Applied ML for Trading Signals
REQ-29603 in the subject
Overview
We are seeking an applied ML engineer to develop, optimize, and deploy machine learning models for alpha generation within a newly formed systematic equities pod deploying intraday mean reversion and microstructure strategies.
The focus is on tree-based ensemble methods (LightGBM, XGBoost, CatBoost) and classical ML pipelines applied to high-frequency financial data. You will work closely with the Portfolio Manager and quantitative researchers to tum ML models into live trading signals.
Principal Responsibilities
• Develop and optimize tree-based ensemble models (LightGBM, XGBoost, CatBoost) for intraday alpha prediction
• Design and implement end-to-end ML pipelines: feature engineering, training, validation, deployment, and monitoring
• Build robust cross-validation frameworks adapted to financial time-series (purged k-fold, walk-forward)
• Engineer features from market microstructure data: order flow imbalance, spread dynamics, volume patterns, cross-asset signals
• Implement model explainability tools (SHAP, feature importance) to understand and validate signal sources
• Optimize model inference for low-latency production deployment
• Monitor model performance in production: detect drift, staleness, and regime changes
• Collaborate with the C++ developer to integrate ML predictions into the real-time trading engine
• Experiment with TabPFN and other rapid-prototyping tools for fast signal discovery
Required Skills / Qualifications
• Master's degree in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field
• 3+ years of experience building and deploying ML models in a production environment, preferably In finance
• Deep expertise in tree-based ensemble methods: LightGBM, XGBoost, CatBoost
including hyperparameter tuning, regularization, and feature selection
• Strong programming skills in Python with proficiency in scikit-learn, Polars/Pandas, NumPy
• Strong understanding of overfitting, data leakage, and proper evaluation methodology for financial time-series
• Strong analytical thinking, attention to detail, and intellectual curiosity
• Excellent communication skills and ability to explain model behavior to non-ML stakeholders
• Familiarity with Al-assisted development tools (Cursor, Claude Code)
Preferred Skills / Experience
• Experience with financial market data (tick data, order book, corporate actions)
• Knowledge of market microstructure and intraday trading dynamics
Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future. When finalizing an offer, we take into consideration an individual's experience level and the qualifications they bring to the role to formulate a competitive total compensation package.
