Senior Engineer - Machine Learning - Regulatory

Cboe Global Markets•Chicago, IL

32d•$154,275 - $199,650•Hybrid

About The Position

Building trusted markets — powered by our people At Cboe Global Markets, we inspire our people to solve complex challenges together because what we do matters. We provide the financial infrastructure that powers the global economy. As a leading provider of market infrastructure and tradable products, Cboe delivers cutting-edge trading, clearing and investment solutions to market participants around the world. We’re building meaningful ways to support professional and personal development while strengthening the trust we’ve earned as a global market leader. Our teams are empowered to share ideas, actively pursue them and bring on a challenge. As champions of internal mobility and access to opportunity, we encourage our people to “go for it” and equip our managers with the training to coach their teams to the next level. We strive to provide employees a safe space to network, share ideas and create opportunities. To support strong partnership and team connection, this role follows a four day in office work model. 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.

Requirements

Bachelor's degree in a quantitative field
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.
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.
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
Excellent written and verbal communication
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
Candidates must be legally authorized to work in the United States without the need for employer sponsorship now or in the future.

Responsibilities

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

Benefits

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
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