Machine Learning Engineer
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