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 and 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 and Computing: Experience with big data technologies (Spark, Dask) SQL and NoSQL databases Cloud platforms (AWS, GCP, Azure)