Senior Machine Learning Engineer (Regulatory)

Join Cboe Global Markets as a Senior Machine Learning Engineer - Regulatory. In this role, you'll work with a skilled team to prototype, train, and deploy ML models and AI applications that monitor financial markets. You'll have the opportunity to utilize advanced AI tools and scalable data engineering to transform complex data into actionable insights. Your responsibilities will include developing and operating AI agent systems, building production-quality data pipelines, and raising the engineering bar through code review and mentorship. If you're passionate about leveraging AI and ML to ensure the integrity of global financial markets, apply now.

Missions

• Prototyping, training, and deploying machine learning models and AI applications to monitor financial markets.
• Collaborating with the team on machine learning experiments across order book analysis, alert detection, and sequential financial data.
• Owning and evolving the team's machine learning training and deployment infrastructure on Snowflake, and building production-quality data pipelines.

Profil recherché

- Data-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 for

- Strong SQL and experience with large-scale datasets

- Production 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 live

- Deep learning applied to temporal or representation problems - sequence models, embeddings/similarity over time-series, or equivalent

- Excellent written and verbal communication

- Solid 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 constraints

- Bachelor's degree in a quantitative field

- We 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 management

- LLMs: building with LLM APIs in production, prompt, context, and harness engineering as an engineering discipline, agent orchestration, full stack development using coding agents

- Classical ML: scikit-learn, weakly supervised clustering and anomaly detection, feature engineering, model evaluation for production decision systems

- Time 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)