Job Description: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's Regulatory Division directly contributes to the company's success by promoting fair, transparent, and trusted markets, through effective and efficient market oversight. We operate surveillance, examination, and investigative programs aimed at detecting and disciplining, or preventing, violative behavior.
Are you passionate about leveraging cutting-edge Artificial 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'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. 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 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
The ideal candidate has: - Bachelor's degree in a quantitative field
- 5+ years of professional software engineering experience, primarily in Python
- Strong SQL skills and experience working with large-scale datasets
- Production ML experience where you've trained, deployed, and monitored models 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 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
- 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)
- Classical ML: scikit-learn, weakly supervised clustering and anomaly detection, feature engineering, model evaluation for production decision systems
Benefits and Perks of working for Cboe Global Markets We value the total wellbeing of our people - including health, financial, personal and social wellness. We believe standard benefits like health insurance and fair pay are a given at any organization. Still, you should know we offer:
- Fair and competitive salary and incentive compensation packages with an upside for overachievement
- Generous paid time off, including vacation, personal days, sick days and annual community service days
- Health, dental and vision benefits, including access to telemedicine and mental health services
- 2:1 401(k) match, up to 8% match immediately upon hire
- Discounted Employee Stock Purchase Plan
- Tax Savings Accounts for health, dependent and transportation
- Employee referral bonus program
- Volunteer opportunities to help you give back to your communities
Some of our associates' favorite benefits and perks include:
- Complimentary lunch, snacks and coffee in any Cboe office
- Paid Tuition assistance and education opportunities
- Generous charitable giving company match
- Paid parental leave and fertility benefits
- On-site gyms and discounts to other fitness centers
