Senior Engineer - Machine Learning - Regulatory

Job Description:

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.

Location Overview

Cboe HQ is located in the historic Old Post Office district, i t’ s a landmark that blends classic architecture with modern amenities. The building features expansive spaces with high ceilings and large windows, offering an abundance of natural light and panoramic views of the city skyline and the Chicago River.

With its prime location in the heart of downtown, the OPO Building provides easy access to major transportation hubs, including Union Station and multiple CTA lines, making it convenient for commuters. The building is home to a variety of amenities, including restaurants, a fitness center, and collaborative workspaces, creating a vibrant and dynamic work environment in one of Chicago's most iconic areas.

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

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

Machine Learning Skills

We work across deep learning, LLM agent systems, and classical ML. While y ou 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

Paid Time Off

More About Cboe Global Markets

We’re reimagining the future of the workplace by focusing on what matters most, our people. Our journey is an inclusive one. We’re investing deeply in leadership programs and career development initiatives that ensure everyone has an equal chance to su