Interested in this AI/ML Engineer role at Cummins?
Apply Now →About This Role
We are looking for a talented School to Work \- IT student worker, to join our team specializing Information Technology , supporting our Cummins Drivetrain and Braking Systems Business Unit facility in Florence, KY
The School to Work\- IT student worker will define and deliver AI\-driven workflows that reshape how work is executed across CDBS Aftermarket. Build and deploy LLM\-powered agents that automate decision\-making, orchestrate tasks, and integrate with enterprise systems. Combine LLM capabilities and enterprise data to improve decision speed, reduce manual effort, and embed domain knowledge into daily operations. Develop and deliver AI training programs to scale adoption and capability across the CDBS Aftermarket organization
In this role, you will make an impact in the following ways:
- Drive business transformation through AI by owning the end\-to\-end delivery of AI agents and workflows that solve critical CDBS Aftermarket business challenges and create measurable operational improvements.
- Build and deploy intelligent solutions by designing, developing, and implementing LLM\-powered agents integrated with enterprise systems to automate work and enhance decision\-making.
- Improve operational efficiency by creating standardized AI workflows that reduce rework, streamline processes, and improve the flow of information across teams
- Translate business needs into measurable outcomes by identifying opportunities, defining success metrics, and delivering solutions that improve cycle time, accuracy, productivity, and user adoption.
- Deliver production\-ready AI capabilities by serving as a hands\-on builder who takes ownership for solution performance, reliability, scalability, and business impact.
- Accelerate organizational adoption of AI by developing and delivering training programs that enable employees to effectively use AI agents and workflows in their daily work
- Bridge business strategy and technology by combining product\-thinking, stakeholder collaboration, and technical expertise to ensure solutions address real business needs and deliver tangible value.
- Foster a culture of continuous improvement and innovation by leveraging emerging AI technologies, agent architectures, and lean execution principles to drive ongoing operational excellence and competitive advantage.
To be successful in this role, you will need to:
- Master AI agent and solution architecture by designing and building LLM\-powered agents that effectively translate business challenges into scalable, automated workflows with clear inputs, outputs, and measurable value.
- Apply workflow design and lean principles by mapping current\-state processes, identifying waste and inefficiencies, and creating AI\-enabled workflows that improve flow, standardization, and continuous improvement.
- Think like a product owner by defining user journeys, designing intuitive AI interactions, and ensuring solutions are practical, trusted, and aligned with user needs.
- Focus relentlessly on adoption and user experience by gathering feedback, iterating solutions quickly, and creating AI tools that employees find valuable, reliable, and easy to use.
- Own delivery and business outcomes by leading initiatives from concept through production deployment while tracking key metrics such as cycle time reduction, productivity gains, accuracy improvements, and adoption rates.
- Communicate effectively with technical and business stakeholders by clearly explaining progress, risks, trade\-offs, and complex AI concepts in a way that supports informed decision\-making.
- Build organizational AI capability by developing training programs, playbooks, standards, and best practices that enable engineers, analysts, and business users to confidently leverage AI solutions.
- Combine strategic thinking with technical expertise by leveraging knowledge of LLM systems, retrieval architectures, agent frameworks, workflow design, and enterprise integrations to deliver scalable solutions that drive lasting business transformation.
Qualifications and Information
- Employment type: Full time or Part Time
- Degree Type: BS, Masters (Preferred), MS
- Educational Program: Data Science, Computer Science, AI/ML, Business information systems, or Business Analytics
- Start date: August or September 2026
- Work Location: Florence, Kentucky, Onsite 3 days a week.
Job Systems/Information Technology
Organization Cummins Inc.
Role Category On\-site with Flexibility
Job Type Student \- School To Work
ReqID 2434239
Relocation Package No
100% On\-Site No
Cummins and E\-Verify
At Cummins, we are an equal opportunity and affirmative action employer dedicated to diversity in the workplace. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, gender, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity and/or expression, or other status protected by law. Cummins validates the right to work using E\-Verify and will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS), with information from each new employee’s Form I\-9 to confirm work authorization. Visit http://EEOC.gov to know your rights on workplace discrimination.
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 Cummins, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Cummins AI Hiring
Cummins has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Florence, KY, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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