Machine Learning Engineer

Machine learning Quantitative Engineer London - Hybrid working Rate - £1,200 Key Responsibilities • Research, design, and implement machine learning and quantitative models for pricing, trading signals, and risk management across Fixed Income products (rates, credit, FX, mortgages). • Apply advanced statistical learning methods (time-series, NLP, deep learning, reinforcement learning, graph-based models) to large-scale, high-frequency, and alternative datasets. • Engineer robust data pipelines and real-time model deployment frameworks to support production trading environments. • Collaborate with traders, quants, and technologists to prototype and scale strategies from research to execution. • Conduct rigorous backtesting, performance analysis, and explainability assessments of machine learning models. • Contribute to the development of quantitative libraries and shared research infrastructure. Qualifications & Skills Essential: • Strong expertise in machine learning, statistical modelling, and numerical methods with practical applications. • Proficiency in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow) and experience with C++ or Java for high-performance model integration. • Solid understanding of Fixed Income products, yield curve modelling, and financial mathematics. • Experience building production-level ML systems in low-latency or large-scale environments. • Strong communication skills with the ability to interact effectively with both technical and trading stakeholders. Desirable: • Previous front-office or systematic trading desk experience. • Familiarity with modern MLOps (Docker, Kubernetes, MLflow, Airflow) and distributed computing (Spark, Ray). • Experience with alpha signal generation, regime detection, or portfolio optimization. • Exposure to alternative/ESG datasets, macroeconomic indicators, and sentiment analysis.