Sr Engineer - Machine Learning - Regulatory

Role Overview

Cboe Global Markets is the world's go-to derivatives and exchange network, providing trading solutions and products in multiple asset classes, including equities, derivatives, FX, and digital assets.Cboe’sRegulatory Division directly contributes to the company’s success by promoting fair, transparent, and trusted markets, through effective and efficient market oversight. Weoperatesurveillance, examination, and investigative programs aimed at detecting and disciplining, or preventing, violative behavior. 

Are you passionate aboutleveragingcutting-edgeArtificial Intelligence and Machine Learning to ensure the integrity and transparency of global financial markets? As a Senior Machine Learning Engineer - Regulatory at Cboe Global Markets,you’llhave the opportunity to work with a highly skilled team to prototype, train, and deploy ML models and AI applications thatmonitorfinancial markets generating terabytes of new data every trading day.You'llbe at the forefront of innovation,utilizingadvanced AI tools and scalable data engineering to transform complex data into actionable insights. 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—apply now and help us turn data into value.

Your responsibilities will be:

• Collaborate with the team onmachinelearning experiments across order book analysis, alert detection, and sequential financial data

• Develop andoperateAI 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

The ideal candidate has:

• Bachelor's degree ina quantitativefield

• 5+ years of professional software engineering experience, primarily in Python

• Strong SQL skills and experience working with large-scale datasets

• Production ML experience whereyou'vetrained, deployed, andmonitoredmodels at scale

• Experience with at least one enterprise cloud data platform (Snowflake, Databricks,BigQuery, or similar) including working within complex RBAC and governance constraints

• Experience with production software development practices: version control, automated testing, CI/CD

• Experience with containerized workflows (Docker)

• Demonstrated ability to mentor other engineers and influence engineering culture on a team

• Excellent written and verbal communication skills

Machine Learning Skills

We work across deep learning, LLM agent systems, and classical ML.While youdon'tneedto knowall of these, you should have real depth in at least a coupleof 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

• Time series and sequential modeling: TCNs, tr