Senior ML Engineer - Regulatory AI for Market Oversight

Cboe Global Markets, Inc.; Chicago (IL); On-site; USD 154,000 - 200,000; Full time

Benefits offered by this job

Health benefits

401(k) match

Tuition assistance

Paid time off

On-site gym

Job summary

Cboe Global Markets is seeking a Senior Machine Learning Engineer - Regulatory to prototype, train, and deploy ML models that monitor financial markets. You’ll work with a skilled team to handle terabytes of data daily, building AI agent systems and scalable data pipelines in on-prem and cloud environments.

The role emphasizes production ML, time-series data, and strong software engineering with Python, SQL, and modern data platforms.

Qualifications

• Bachelor's degree in a quantitative field.
• Production ML experience with time-series / sequential data.
• Deep learning applied to temporal problems.
• Data-driven mindset for feature selection and model input.
• Strong SQL and large-scale data handling.
• 5+ years software engineering in Python with production practices.

Responsibilities

• Collaborate on ML experiments across order book analysis and sequential data.
• Develop and operate AI agent systems in production.
• Own ML training and deployment infrastructure on Snowflake.
• Build data pipelines for terabytes of daily market data.
• Mentor junior engineers and perform code reviews.
• Develop production-quality, test-driven Python code.
• Develop explainability and compliance solutions for AI/ML.
• Track ML model performance across training, validation, and inference.

Skills

Communication

Bachelor’s degree

Time-series ML

Deep learning

SQL

Python

Production ML

ML Ops

Education

Bachelor's degree in a quantitative field

Tools

Snowflake

Databricks

BigQuery

Docker

Job description

Cboe Global Markets is seeking a Senior Machine Learning Engineer - Regulatory to prototype, train, and deploy ML models that monitor financial markets. You’ll work with a skilled team to handle terabytes of data daily, building AI agent systems and scalable data pipelines in on-prem and cloud environments.

The role emphasizes production ML, time-series data, and strong software engineering with Python, SQL, and modern data platforms.