Senior Machine Learning Engineer (Regulatory)

As a Senior Machine Learning Engineer - Regulatory at Cboe Global Markets, you’ll have the opportunity to work with a highly skilled team to prototype, train, and deploy ML models and AI applications that monitor financial markets generating terabytes of new data every trading day You’ll be at the forefront of innovation, utilizing advanced AI tools and scalable data engineering to transform complex data into actionable insights Collaborate with the team on machine learning experiments across order book analysis, alert detection, and sequential financial data Develop and operate AI agent systems in production, applying ML engineering discipline to nondeterministic LLM-based software development workflows Own and evolve the team’s ML training and deployment infrastructure on Snowflake Build production-quality data pipelines for processing terabytes of daily financial market data Raise the engineering bar through rigorous code review, architecture guidance, and mentorship of junior and mid-level engineers Design and develop production-quality, test-driven Python code Develop explainability and process-compliance solutions for AI and ML Effectively track and evaluate ML model performance across training, validation, inference, and monitoring Work in both on-premises and cloud environments Work closely with complementary engineering teams Produce clear and thorough documentation, including ML proposals, experiment specifications, technical design, and testing scenarios Communicate technical information clearly and concisely to both technical and end-user audiences If you thrive on tackling real-world challenges, excel in programming and large-scale data operations, and want to make a meaningful impact in a fast-paced, highly regulated environment, this is your chance to join a team where your expertise will help shape the future of market oversight. Step into a role where your ideas drive progress, and your contributions truly matter-Passionate about leveraging cutting-edge Artificial Intelligence and Machine Learning to ensure the integrity and transparency of global financial marketsData-reasoning instinct - able to say what the data is telling you and what data should go into a model in the first place, not just which model to reach forStrong SQL and experience with large-scale datasetsProduction ML experience with time-series / sequential data - you’ve trained, deployed, and monitored models at scale, and you understand how time affects the structure of data: stationarity, regime change, leakage, and why a model that looks good in backtest fails liveDeep learning applied to temporal or representation problems - sequence models, embeddings/similarity over time-series, or equivalentExcellent written and verbal communicationSolid software-engineering foundation: 5+ years, primarily Python, with production practices (version control, automated testing, CI/CD, Docker) and comfort in an enterprise cloud data platform (Snowflake / Databricks / BigQuery, etc.) under real RBAC and governance constraintsBachelor’s degree in a quantitative fieldWe work across deep learning, LLM agent systems, and classical ML. While you don’t need to know all of these, you should have real depth in at least a couple of these, and curiosity about the rest: Deep learning: PyTorch, custom training loops, architecture design and experimentation, multi-GPU distributed ML, experiment tracking, model lifecycle managementLLMs: building with LLM APIs in production, prompt, context, and harness engineering as an engineering discipline, agent orchestration, full stack development using coding agentsClassical ML: scikit-learn, weakly supervised clustering and anomaly detection, feature engineering, model evaluation for production decision systemsTime series and sequential modeling: TCNs, transformers, time-contrastive learning, or similar approaches on temporal data, as well as classical time series modeling (e.g. ARIMA)