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The Marmon Group LLC
As a part of the global industrial organization Marmon Holdings—which is backed by Berkshire Hathaway—you’ll be doing things that matter, leading at every level, and winning a better way. We’re committed to making a positive impact on the world, providing you with diverse learning and working opportunities, and fostering a culture where everyone’s empowered to be their best.
Position Summary
The AI organization is seeking an AI Project Management Intern to support the coordination and delivery of artificial intelligence initiatives across business, technology, data, security, and support teams. This internship is designed for a student who is currently attending school and interested in learning how AI projects are identified, planned, developed, tested, deployed, governed, and adopted in a corporate environment.
This role will work closely with AI project managers, business stakeholders, data and engineering teams, application teams, cybersecurity, vendors, and end users to help keep AI projects organized, documented, measurable, and moving forward. The ideal candidate is organized, curious, detail\-oriented, comfortable communicating with others, and eager to learn practical project management skills in an AI delivery environment.
What You Will Support
- AI Project Coordination: Assist with AI project plans, timelines, milestones, meeting notes, action items, risks, issues, dependencies, and follow\-ups.
- Use Case Intake \& Prioritization: Help organize AI use case requests, business requirements, expected outcomes, stakeholder input, and prioritization materials.
- Stakeholder Communication: Help prepare meeting summaries, status updates, project documentation, demos, communications, and adoption materials for business and IT teams.
- Testing \& User Feedback: Support user acceptance testing, prompt or workflow validation, issue tracking, feedback collection, and readiness checklists.
- Responsible AI \& Governance Support: Help maintain documentation related to data sources, access, risks, approvals, review checkpoints, and responsible AI considerations.
- Process Improvement: Help organize templates, trackers, project files, lessons learned, reusable artifacts, and repeatable AI delivery practices.
Key Responsibilities
- Support AI project managers and team members with day\-to\-day project coordination activities.
- Help maintain project plans, task trackers, timelines, status reports, decision logs, risk logs, and meeting materials.
- Capture meeting notes, decisions, action items, owners, target dates, open questions, and follow\-ups.
- Assist with documenting AI use cases, business requirements, user stories, acceptance criteria, process notes, and stakeholder feedback.
- Help coordinate discovery sessions, demos, testing sessions, training discussions, and project check\-ins.
- Support testing and validation activities, including test scripts, UAT feedback logs, defect tracking, prompt or workflow observations, and readiness items.
- Organize and maintain AI project materials in shared workspaces so project teams can easily find current information.
- Assist with adoption planning, communication drafts, training material preparation, user guides, and post\-launch feedback collection.
- Help track project metrics, business outcomes, adoption signals, and lessons learned where available.
- Learn and follow IT governance, cybersecurity, privacy, compliance, documentation, and responsible AI practices.
- Contribute ideas to improve AI project coordination, communication, repeatable delivery methods, and stakeholder experience.
What You Bring
- Organization \& Follow\-Through: Ability to manage details, track action items, keep information organized, and help move tasks forward.
- Communication Skills: Clear written and verbal communication with the ability to summarize information professionally for business and technical audiences.
- Curiosity About AI: Interest in AI, automation, data, business processes, user experience, and how technology can improve work.
- Attention to Detail: Ability to capture accurate notes, maintain documentation, identify missing information, and spot inconsistencies.
- Collaboration: Comfortable working with business users, IT teams, data teams, engineers, project stakeholders, and vendors.
- Problem\-Solving Mindset: Ability to ask thoughtful questions, identify blockers, organize next steps, and learn from feedback.
- Responsible AI Awareness: Willingness to learn about data sensitivity, access, security, privacy, compliance, transparency, and safe use of AI solutions.
Education \& Experience
- Currently pursuing a degree in Business, Information Systems, Computer Science, Data Science, Engineering, Project Management, or a related field.
- Able to provide own housing and transportation for the duration of the internship.
- Must be actively enrolled in a degree seeking program during the internship or returning to school afterward. Successfully complete a background check and drug screen as a condition of employment.
- Interest in AI project management, business analysis, technology delivery, data\-driven solutions, automation, or digital transformation.
- Prior internship, school project, volunteer, or part\-time experience involving coordination, documentation, technology, data, or teamwork is helpful but not required.
- Familiarity with Microsoft Office tools such as Outlook, Teams, Word, Excel, and PowerPoint.
- Exposure to project management tools, Agile, Scrum, Waterfall, business analysis concepts, AI tools, data visualization, or low\-code platforms is a plus but not required.
- No expectation to independently build AI models or lead complex projects; this role is focused on learning, coordination, documentation, testing support, and stakeholder communication.
Success in This Role Looks Like
- AI project notes, action items, trackers, and documentation are accurate, organized, and kept up to date.
- Project managers and team members have better visibility into open tasks, decisions, risks, dependencies, and next steps.
- Stakeholder meetings, demos, testing sessions, and follow\-ups are supported with clear materials and communication.
- Testing, adoption, training, and deployment readiness activities are easier to coordinate.
- Responsible AI, security, compliance, and documentation considerations are captured and escalated appropriately.
- The intern gains practical experience in AI project delivery, stakeholder collaboration, project management fundamentals, and responsible AI practices.
Internship Expectations
This role is intended to provide a meaningful learning experience while supporting real AI project work. The intern will not be expected to independently lead complex projects or serve as the owner of AI solution delivery, but will be expected to take ownership of assigned coordination tasks, communicate clearly, meet agreed deadlines, ask questions, and actively learn from AI project managers and team members.
Pay Range:
28\.64 \- 35\.00
We offer a comprehensive benefits package that may include medical, dental, vision, 401k matching, and more!
Following receipt of a conditional offer of employment, candidates will be required to complete additional job\-related screening processes as permitted or required by applicable law.
We are an equal opportunity employer, and all applicants will be considered for employment without attention to their membership in any protected class. If you require any reasonable accommodation to complete your application or any part of the recruiting process, please email your request to [email protected], and please be sure to include the title and the location of the position for which you are applying.
Salary Context
This $58K-$72K range is in the lower quartile 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 Marmon Group, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($65K) sits 70% below the category median. Disclosed range: $58K to $72K.
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.
Marmon Group AI Hiring
Marmon Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $72K - $72K.
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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