Machine Learning Engineer / ML Engineer Machine Learning Development * Design and implement machine learning models for financial applications, with a focus on pricing and risk analytics * Build scalable ML pipelines for processing large-scale financial data * Develop deep learning architectures for time series prediction, anomaly detection, and pattern recognition in market data * Optimize model performance through advanced techniques including hyperparameter tuning, ensemble methods, and neural architecture search * Collaborate with quants to understand pricing model requirements and identify ML opportunities * Develop data-driven approaches to complement traditional quantitative finance models * Support implementation of ML solutions for derivatives pricing and risk management Core Technical Skills Machine Learning Expertise: * Deep understanding of ML algorithms (supervised/unsupervised learning, reinforcement learning) * Extensive experience with neural networks, including RNNs, LSTMs, Transformers * Expertise in gradient boosting, random forests, and ensemble methods * Experience with generative models (GANs, VAEs, Diffusion models) Programming & Tools: * Expert-level Python programming * Proficiency with ML frameworks (PyTorch, TensorFlow, JAX) * Experience with scikit-learn, XGBoost, LightGBM * Strong software engineering practices and clean code principles Data & Computing: * Experience with big data technologies (Spark, Dask) * SQL and NoSQL databases * Cloud platforms (AWS, GCP, Azure) Experience * Track record of successfully deployed ML models at scale * Experience with time series analysis and forecasting * Experience applying ML in finance, trading, or risk management contexts * Knowledge of stochastic processes and their applications Financial Knowledge * General understanding of financial markets and instruments * Basic knowledge of derivatives and their risks * Awareness of risk management principles