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About This Role
Job Description:
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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, it’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
As a Senior AI Engineer, you will play a central role in integrating AI into our core operations and developing AI\-native products. You'll own the full lifecycle of AI initiatives from sitting with business stakeholders to scope and discover opportunities through to hands\-on design, development, and deployment of production\-grade systems. You won't just be building agents; you'll develop and define reusable services for agents across Cboe.
This is a high\-ownership, high\-impact role for someone who wants to operate independently, translate organizational strategy into concrete technical outcomes, and bring others along with them. You'll be expected to work independently and consider the impact of technology on strategy. You'll bring both deep technical expertise and strong communication skills.
In addition to pushing our AI initiatives forward, you'll mentor other engineers on the team and help establish the standard for how we build AI at Cboe.
Your responsibilities will be:
- Design, build, and deploy production\-ready AI agents and agentic systems across Cboe's internal platforms and workflows.
- Own scoping and discovery for AI initiatives — partnering directly with non\-technical business stakeholders to understand needs and convert them into well\-defined technical solutions.
- Build and enhance Cboe's agentic control plane.
- Apply context engineering principles (prompt design, memory architecture, retrieval strategy) to build reliable, high\-performing AI systems.
- Establish and uphold best practices for agent development, evaluation, and production observability.
- Drive architecture and design decisions for agentic systems, owning tradeoffs across scalability, reliability, and risk across multiple teams.
- Design and implement benchmarking and evaluation frameworks to measure and continuously improve AI system performance.
- Translate Cboe's AI strategy into executable, prioritized technical work.
- Lead technical design reviews and mentor engineers across the team, setting the standard for how AI systems are built at Cboe.
- Produce clear technical documentation including scoping artifacts, design specs, and testing criteria.
The ideal candidate has
- Either: A Bachelor's or Master's degree in Computer Science, Engineering, or related field OR Equivalent demonstrated experience
- 7–10\+ years of relevant professional experience in software engineering, with increasing seniority
- 3\+ years of professional experience in applied AI (building agents, MCP servers, context/harness engineering)
- Proven experience building and deploying AI solutions in production environments, including familiarity with agent frameworks and a clear understanding of what they do and why they matter
- Hands\-on experience building tools and integrations for LLM\-based systems
- Deep proficiency in context engineering including prompt design, retrieval strategies, and memory/context management
- Demonstrated ability to work directly with non\-technical stakeholders to scope AI opportunities and define technical requirements
- Strong programming skills (language agnostic, Python strongly preferred)
- Experience with LLM evaluation, observability, and benchmarking as core engineering practice
- Ability to work independently, manage ambiguity, and deliver results without close oversight
- Track record of setting technical standards, leading design/architecture reviews, and mentoring engineers. This role carries a formal technical leadership expectation
- Strong written and verbal communication skills, particularly the ability to explain complex AI concepts to non\-technical audiences
You'll really stand out if you have:
- 10\-15\+ years of overall professional experience, including 5\+ years in applied AI
- Experience in a consulting, platform, or internal services capacity and experience managing multiple workstreams
- Prior experience owning an AI scoping or discovery practice, not just executing against pre\-defined specs
- Familiarity with AWS and/or Snowflake in a production context
- Experience with NLP tasks such as summarization, classification, or entity extraction and their application in agentic contexts
- Contributions to AI strategy, roadmap definition, or architectural decision\-making at an organizational level
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 succeed.
We work with purpose, solving problems with ingenuity, collaboration, and a lot of passion. We’re an engaged and excited team connecting markets across borders and embracing growth in all its forms to achieve incredible outcomes.
Equal Employment Opportunity
We're proud to be an equal opportunity employer do not discriminate against any employee or applicant for employment based on any legally protected characteristic, including race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, or veteran status. We are committed to fostering a workplace where all individuals are valued and respected.
\#LI\-CP1
This position is not eligible for visa sponsorship. Candidates must be legally authorized to work in the United States without the need for employer sponsorship now or in the future.
Salary Ranges (applicable for US locations only)
At Cboe, we are committed to providing a competitive, transparent, and market‑informed total rewards program. The anticipated base salary range for this role is $140,250\-$181,500, with actual compensation determined by job‑related factors such as skills, relevant experience, education, internal alignment, and location.
This role may also be eligible for annual incentive compensation and, where applicable, participation in Cboe's long\-term equity programs.
Additional information about Cboe's total rewards program, including benefits and other compensation components, can be found here: Total Rewards at CBOE.
*Any communication from Cboe regarding this position will only come from a Cboe recruiter who has a @cboe.com email or via LinkedIn Recruiter. Cboe does not use any other third party communication tools for recruiting purposes.*
Salary Context
This $140K-$181K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cboe Global Markets, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $140K to $181K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cboe Global Markets AI Hiring
Cboe Global Markets has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $181K - $181K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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