> Markdown version of [/jobs/ext/2260332-machine-learning-quant-engineer](https://www.wearedevelopers.com/jobs/ext/2260332-machine-learning-quant-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Quant Engineer - **Company:** Michael Page - **Location:** London, UK - **Experience:** Starter - **Salary:** £312,000.0 - **Contract:** Temporary contract - **Skills:** Amazon Web Services, Microsoft Azure, Big Data, Information Engineering, Python (Programming Language), Machine Learning, NoSQL, Pattern Recognition, Tensorflow, SQL Databases, Reinforcement Learning, Google Cloud, Pytorch, Apache Spark, Deep Learning, Scikit Learn, Xgboost, Dask, Machine Learning Operations, Multiaccess Edge Computing - **Published:** August 26, 2026 - **Apply:** https://www.collegerecruiter.com/job/2815058353-machine-learning-quant-engineer ## About the Role * Advanced Machine Learning Expertise - Demonstrates deep understanding of ML algorithms (supervised, unsupervised, reinforcement learning) and has hands-on experience with deep learning architectures like RNNs, LSTMs, and Transformers. * Strong Financial Domain Knowledge - Understands financial instruments, derivatives, and risk management principles, with experience applying ML in trading, pricing, or risk analytics contexts. * Technical Proficiency - Expert in Python and familiar with ML frameworks such as PyTorch, TensorFlow, and JAX. Skilled in using tools like scikit-learn, XGBoost, and LightGBM. * Data Engineering & Infrastructure Skills - Comfortable working with big data technologies (Spark, Dask), SQL/NoSQL databases, and cloud platforms (AWS, GCP, Azure). Able to build scalable ML pipelines for large-scale financial data. * Model Optimisation & Deployment Experience - Proven track record of deploying ML models at scale, with experience in hyper-parameter tuning, ensemble methods, and neural architecture search. * Collaborative & Business-Focused - Works effectively with quants and stakeholders to translate financial requirements into ML solutions. Communicates insights clearly and aligns models with strategic business goals. * Innovative & Analytical Mindset - Capable of developing data-driven approaches that complement traditional quantitative models and drive measurable impact in pricing and risk analytics. ## Description Base pay range This temporary role requires an ML Quant Engineer with expertise within an Investment Bank. The position is based in London and involves developing and implementing machine learning models to support financial decision-making. Description * Design and implement machine learning models for financial applications, with a focus on derivatives pricing, risk analytics, and market forecasting. * Build scalable ML pipelines to process large volumes of financial data efficiently. * Develop deep learning architectures for time series prediction, anomaly detection, and pattern recognition in market data. * Optimise model performance using techniques such as hyper-parameter tuning, ensemble methods, and neural architecture search. * Collaborate with quantitative analysts to align ML models with pricing methodologies and identify opportunities for innovation. * Support the deployment of ML solutions into production systems for real-time risk management and pricing automation. Profile * Advanced Machine Learning Expertise - Demonstrates deep understanding of ML algorithms (supervised, unsupervised, reinforcement learning) and has hands-on experience with deep learning architectures like RNNs, LSTMs, and Transformers. * Strong Financial Domain Knowledge - Understands financial instruments, derivatives, and risk management principles, with experience applying ML in trading, pricing, or risk analytics contexts. * Technical Proficiency - Expert in Python and familiar with ML frameworks such as PyTorch, TensorFlow, and JAX. Skilled in using tools like scikit-learn, XGBoost, and LightGBM. * Data Engineering & Infrastructure Skills - Comfortable working with big data technologies (Spark, Dask), SQL/NoSQL databases, and cloud platforms (AWS, GCP, Azure). Able to build scalable ML pipelines for large-scale financial data. * Model Optimisation & Deployment Experience - Proven track record of deploying ML models at scale, with experience in hyper-parameter tuning, ensemble methods, and neural architecture search. * Collaborative & Business-Focused - Works effectively with quants and stakeholders to translate financial requirements into ML solutions. Communicates insights clearly and aligns models with strategic business goals. * Innovative & Analytical Mindset - Capable of developing data-driven approaches that complement traditional quantitative models and drive measurable impact in pricing and risk analytics. Job Offer * A competitive daily rate up to £1200 per day (inside IR35), depending on experience. * The opportunity to work on cutting-edge machine learning projects in the financial services industry. * A temporary role offering valuable exposure to a global organisation in London. * BASED 4 DAYS PER WEEK IN THE OFFICE (Central London) Seniority level Entry level Employment type Temporary Job function Finance Industries Investment Banking ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained) - [NoSQL Data Modeling for Front-end Developers](https://www.wearedevelopers.com/videos/297-nosql-data-modeling-for-front-end-developers) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [The Fastest-Growing Tech Sectors to Look Out for in 2025](https://www.wearedevelopers.com/magazine/373-the-fastest-growing-tech-sectors-to-look-out-for-in-2025) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)