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Urgent! Machine Learning Intern, Regulatory Job Opening In Chicago – Now Hiring Cboe Global Markets, Inc.

Machine Learning Intern, Regulatory



Job description

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.

Cboe interns work with a variety of staff across multiple departments and have the opportunity to put their skills to work in their field of interest, while learning about Exchange technology and operations through our robust Options Institute courses.

The three main foundational pillars of our internship program are: develop, educate and network.

We want to ensure each of our interns receive a real-world working experience that encourages academic, professional and personal growth.

Candidates should be versatile, eager and able to work in a fast-paced, time-sensitive financial and technical environment.

Our interns will have the flexibility of working 2 days remotely, and 3 days in office per week at one of our state-of-the-art offices in Chicago, Kansas City, and New York City.

To be eligible for this internship, applicants must be enrolled in a university or college program and should not be scheduled to graduate before December of the internship year.

Our internship program runs from June to August and you will wrap up your internship with a final presentation and retreat.

Visit our student page for more information about our internship program!

The Regulatory Technology team is hiring for a Machine Learning intern.

The Regulatory Machine Learning intern at Cboe will work on prototyping, training, testing and validating classical- and neural-network-based machine learning models and LLM-driven applications for the surveillance of financial markets, directly processing and helping users make sense of terabytes of new data and trading patterns every day.

A successful candidate will have extreme intellectual curiosity, tolerance for uncertainty, perseverance, good academic knowledge of machine learning models (classical, deep learning, and LLMs) and statistical techniques and will demonstrate strong programming and large-scale data engineering skills.

Together, we’ll research and deploy new techniques to detect violative behavior in financial markets.

Your responsibilities and learning objectives will be:

  • Train various candidate models to fit a given business problem
  • Prepare data sets and design data features for ML input in conjunction with financial surveillance experts
  • Effectively track and evaluate ML model performance during research phase
  • Contribute creative ideas for problem solving during brainstorming discussions with ML team and users
  • Receive and implement constructive feedback through rigorous code reviews, QA testing, and model evaluation
  • Work in both on-premises and cloud environments
  • Produce clear and thorough documentation for your research and analytical work
  • Communicate technical information clearly and concisely to an end-user audience
  • Learn best practices in software engineering and ML research
  • The ideal candidate has:

  • Understanding of a wide variety of machine learning algorithms, supervised and unsupervised, classical and deep learning, including modern large language models and their unique infrastructure requirements
  • Fluency with advanced undergraduate-level mathematics, including statistics, linear algebra, and multivariable calculus, to the extent of being able to read and implement ideas found in published ML papers
  • Familiarity with writing code to support the various lifecycle phases of machine learning projects such as training, validation, inference, and production monitoring
  • Strong Python-based programming and data engineering skills
  • Strong SQL knowledge (Snowflake experience is a plus)
  • Experience with common data science and ML libraries, such as numpy, pandas, Spark, scikit-learn, TensorFlow, and PyTorch
  • Experience configuring and using AI agents to assist with coding and research tasks
  • Experience developing and deploying AI agents/workflows for knowledge-intensive tasks with stringent correctness requirements
  • Ability to work both independently and as part of a team
  • Excellent written and verbal communication skills
  • Demonstrates critical thinking, attention to detail, and good judgment
  • Bachelor’s or Master’s degree in progress in a quantitative field and should not be scheduled to graduate before December of the year in which the internship takes place
  • You’ll really stand out with:

  • Strong ability to translate and see long-range connections between trade-offs in ML algorithm design and trade-offs in end-user product features
  • Knowledge of time series analysis in a financial context, both statistical methods and deep learning methods
  • Experience in production software development environments, including version control, testing and test-driven development and change management
  • Experience with multi-GPU model training
  • Experience in the financial services sector, or in any highly regulated industry
  • Benefits and Perks

  • Competitive compensation
  • Flexible, hybrid work environment, 3 days in office, 2 days remote, per week.
  • 2:1 401(k) match, up to 8% match immediately upon hire.
  • Some of our employees’ favorite benefits and perks include:

  • Daily complimentary in-office lunch from local restaurants
  • Endless free coffee and snacks to fuel your workday
  • Monthly in office networking events and happy hours
  • Associate Resource Groups (ARGs) and affinity groups for support and community building

  • Required Skill Profession

    Computer Occupations



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