Interested in this AI/ML Engineer role at Cummins?
Apply Now →Skills & Technologies
About This Role
DESCRIPTION
Ensure internal and external customer’s expectations and requirements are met from end to end in each of the supply chains. Manage daily operations across functions to ensure a dependable and efficient supply chain. Lead continuous improvement projects that align with Cummins Operating System and Functional Excellence direction. Promote Corporate Supply Chain initiatives and lead the cross\-Business Unit planning processes to ensure intra\-company alignment. Lead the efforts within one or more Supply Chain functions including materials, production, inventory, logistics, demand management and order fulfillment. Use analytics and algorithms to determine the optimal level of inventory investment across the end\-to\-end supply chain within specific constraints, including warehouse space and customer delivery. Provide guidance and expertise on planning parameter refinement. Utilize advanced tools including Advanced Planning Command Centre and Business Intelligence (BI) Dashboards to make decisions related to parts availability, inventory optimization and root cause analysis. Assess gaps in the supply chain performance against the expectation of achieving target deliverables. Approach daily problem solving and continuous improvement initiatives and opportunities strategically and tactically to achieve performance targets. Develop, understand and improve necessary supply chain Key Performance Indicators (KPIs). Analyze operational trends and develop corrective action plans. Address supply chain failures promptly to ensure operations stability. Prepare and manage an annual operating plan including expense, resources, and capital. Interpret Materials KPIs to understand end\-customer impact and strategically balance trade\-offs to influence one KPI while minimizing the impact to others. Confer with supply chain planners to forecast demand or create supply plans that ensure availability of materials and products. Define performance metrics for measurement, comparison, or evaluation of supply chain factors, such as product cost or quality. Design and develop packaging solutions with consideration for distribution conditions and dynamics to protect products during transportation and storage while ensuring cost\-effectiveness, sustainability, and compliance with regulations. Influence the multi\-disciplined and end\-to\-end integration of the flow of information, products, and services to achieve the desired result for the end customer within the first\-fit or aftermarket business segments while balancing the tensions of inventory, cost, and delivery. Utilize knowledge of industrial packaging materials to work with internal and external customers and suppliers to specify design requirements and management of expendable and returnable packaging and containers for new finished products and raw material components. Work cross\-functionally to oversee and ensure supplier conformance to packaging standards and other regulatory requirements for inbound and outbound shipments. Drive consistent results by applying business acumen in decision making and cost\-based analysis. Coordinate end\-user testing of packaging solutions to ensure suitability for manufacturing operations and storage.
RESPONSIBILITIES
Positions require a Master’s degree in Industrial or Packaging Engineering, or related field and 3 years of experience as an Industrial, Manufacturing or Packaging Engineer, or related position. Alternatively, the employer will accept a Bachelor’s degree in Industrial or Packaging Engineering, or related field and 6 years of experience as an Industrial, Manufacturing or Packaging Engineer, or related position. Experience to include: Demand management and order fulfillment; Advanced Planning Command Centre and Business Intelligence (BI) Dashboards; Inventory optimization and root cause analysis; Continuous improvement initiatives and opportunities; Supply chain and Materials Key Performance Indicators (KPIs); Develop corrective action plans; Prepare annual operating plan; Ensure availability of materials and products; Balance inventory, cost, and delivery; Industrial Packaging Material knowledge and Returnable Packaging Management; Packaging Distribution Conditions, Dynamics and Testing.
QUALIFICATIONS
Start Date of Posting: 03/19/2026
End Date of Posting: 04/03/2026
Position Type: On\-site
Location: Whitakers, NC
- Annual USD Salary Minimum – Maximum
$134,534 to $140,000
Job Supply Chain Planning
Organization Cummins Inc.
Role Category On\-site with Flexibility
Job Type Exempt \- Experienced
Min Salary $
Max Salary $
ReqID 2426288
Relocation Package Yes
100% On\-Site Yes
Due to the operational nature and specific job duties of this role, work is required to be completed 100% in person/On\-site.
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.
Salary Context
This $134K-$140K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($137K) sits 18% below the category median. Disclosed range: $134K to $140K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Cummins AI Hiring
Cummins has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Whitakers, NC, US, Indianapolis, IN, US. Compensation range: $140K - $180K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.