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Job Title: Artificial Intelligence Intern Department: Information Technology Business Unit: KSE Location: Denver, CO Reports To: Executive Director, IT Infrastructure Employment Type: Full Time Hourly/Non-Exempt Supervisor Position: No Posting End Date: 10/14/2026 _________________ Kroenke Sports & Entertainment (KSE) is an American Sports and Entertainment holding company based in Denver, Colorado. KSE is committed to providing world class sports and entertainment for both live and broadcast audiences. We are the employer of choice as the owner and operator of Ball Arena, DICK’S Sporting Goods Park, the Paramount Theatre, Denver Nuggets (NBA), the Colorado Avalanche (NHL), Colorado Mammoth (NLL), Colorado Rapids (MLS), Altitude Sports & Entertainment, Major League Fishing/Fishing League Worldwide (MLFLW), Winnercomm, Outdoor Sportsman Group and SkyCam. The KSE Information Technology (IT) Department provides a broad range of technology services supporting operations at the enterprise, business unit, department, event, and project levels within the company. The IT department focuses on building and maintaining a reliable and secure technological infrastructure while delivering quality services that are prompt, cost effective, and reliable. Nature of Work: Provides support for a wide variety of Artificial Intelligence initiatives across the enterprise. The AI intern will work closely with a variety of Information Technology professionals to develop and maintain new AI workflows. Examples of work performed:
- Identifies and maintains data and pipelines for supporting AI workflow including data entry, organization, and analysis
- Assists in the development and implementation of AI models and algorithms.
- Conducts research and experiments to improve AI solutions.
- Collaborate with team members to analyze accuracy and reliability of solutions.
- Documents processes and findings for future reference.
- Assists in developing and training machine learning models
- Contribute to algorithm optimization and performance evaluation
- Support the integration of models into production systems
- Participates in solution evaluations, team meetings, and brainstorming sessions
- Stays updated with the latest trends and advancements in AI and machine learning.
This description is a summary only and describes the general level of work being performed, it is not intended to be all-inclusive. The duties of this position may change from time to time and/or based on business needs. We reserve the right to add or delete duties and responsibilities at the discretion of the supervisor and/or hiring authority. Working Conditions & Physical Demands:
- Typical Office Conditions
- Lift up to 20 lbs
- Lift, bend, place, maneuver as necessary to deploy computing equipment
Qualifications
- Minimum + High school diploma or GED, required + Currently enrolled in a (4-year) undergraduate or graduate degree program + Applicants must meet minimum qualifications at the time of hire.
- Preferred + Computer Science, Data Science, AI, Business Administration, Information Systems, Statistics, Math, or similar majors.
Competencies/Knowledge, Skills & Abilities
- Proficient in the use of computers and laptops running the Windows operating system.
- Proficient in the use of Microsoft Technologies including Outlook, Word, Excel, and PowerPoint, and CoPilot.
- Knowledgeable with respect to AI concepts and tools including Machine Learning, Large Language Models, Generative AI, IoT, and Natural Language Processing
- Familiar with the concepts involved in building AI Agents
- Understanding of AI prompting techniques
- Solid understanding of computer networks and data communications, network components, and technical terms.
- Good analytical and investigational skills.
- Excellent organization skills for creating priorities, documenting problems and solutions, following detailed instructions.
Compensation
- Base hourly rate of $18.81 per hour
Benefits Include (For FT Benefits Eligible only)
- 12 Paid Company Holidays
- Health Insurance (Medical, Dental, Vision)
- Paid Time Off (PTO)
- Life Insurance
- Short and Long-term Disability
- Health Savings Account (HSA)
- Flexible Spending plans (FSAs)
- 401K plan
Applications Must be Received by: 10/14/2026 Equal Employment Opportunity Kroenke Sports & Entertainment (KSE) provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
Salary Context
This $37K-$39K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Kroenke Sports & Entertainment, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($38K) sits 82% below the category median. Disclosed range: $37K to $39K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Kroenke Sports & Entertainment AI Hiring
Kroenke Sports & Entertainment has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $39K - $39K.
Location Context
AI roles in Denver pay a median of $197,900 across 126 tracked positions. That's 7% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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