Interested in this AI/ML Engineer role at Eaton?
Apply Now →About This Role
Eaton’s CTO Corporate Technology Office division is currently seeking a Engineering Specialist \- AI/ML for Energy Systems.
The expected annual salary range for this role is $122000 \- $179000 a year.
Please note the salary information shown above is a general guideline only. Salaries are based upon candidate skills, experience, and qualifications, as well as market and business considerations.
\* This role must report on\-site at our Golden, CO research facility. Relocation assistance is available for a qualified candidate currently residing in the USA.
What you’ll do:
-------------------
Job Summary:
Eaton Research Labs (ERL), the corporate R\&D organization within Eaton's Chief Technology Office (CTO), is seeking a Engineering Specialist to help develop next\-generation AI\-enabled solutions for energy systems. This role will focus on applying artificial intelligence, machine learning, digital twins, optimization, and advanced analytics to solve challenges in power systems, data centers, electrification, and distributed energy resources.
This is a high\-impact research role for someone who wants to apply AI/ML to real\-world energy infrastructure challenges. The successful candidate will help shape Eaton’s next generation of intelligent power management technologies—bridging advanced algorithms, energy\-domain expertise, and commercialization pathways across global businesses.
Eaton is a power management company made up of over 92,000 employees, doing business in more than 175 countries. Making what matters work at Eaton takes the passion of every employee around the world. We create an environment where creativity, invention and discovery become reality, each and every day. It’s where bold, bright professionals like you can reach your full potential—and where you can help us reach ours.
This position is based in Golden, Colorado. Relocation support may be considered for candidates outside the local area.
In this role you will:
- Lead innovative research programs focused on AI/ML applications for energy systems.
- Develop AI\-enabled control, optimization, forecasting, and decision\-making algorithms.
- Lead government\-funded and internally funded research projects.
- Collaborate with Eaton businesses, universities, national laboratories, and industry partners.
- Author patents, invention disclosures, technical reports, and publications.
- Present technical results to leadership, customers, and external stakeholders.
- Mentor junior engineers and researchers.
Why Join ERL:
- Influence Eaton's future technology strategy.
- Work directly within the CTO organization.
- Lead breakthrough research programs.
- Collaborate with national laboratories and universities.
- Create intellectual property and industry impact.
- Work on technologies that transition from research concepts into commercial power management products used globally.
Qualifications:
-------------------
Required (Basic) Qualifications:
- Master’s degree in Electrical Engineering or related field from an accredited institution plus 9\+ years of industrial/entrepreneurial experience after Master’s in researching, developing and designing technology solutions in the relevant and adjacent areas: (artificial intelligence, machine learning, digital twins, optimization, and advanced analytics to solve challenges in power systems, data centers, electrification, and distributed energy resources) .
….Or…
- PhD degree in Electrical Engineering or related field from an accredited institution plus 4\+ years of industrial/entrepreneurial/academic experience after Ph.D. in researching, developing and designing technology solutions in the same relevant and adjacent areas.
- Eaton will not consider applicants for employment immigration sponsorship or support for this position. This means that Eaton will not support any CPT, OPT, or STEM OPT plans, F\-1 to H\-1B, H\-1B cap registration, O\-1, E\-3, TN status, I\-485 job portability, etc.”
- Must be authorized to work in the United States without company sponsorship now or in the future.
Preferred Qualifications:
- Demonstrate strong experience in one or more of the following areas related to AI/ML:
o AI/ML for energy systems, controls, optimization, or forecasting
o Digital twins, surrogate modeling, or physics\-informed ML
o LLMs, agentic AI, scalable AI frameworks, or MLOps
- Possess a good foundation in power and energy systems and have demonstrated experience applying AI/ML techniques to these domains.
Skills:
-----------
Position Criteria:
- Collaborative orientation.
- Self\-motivated and goal\-oriented individual.
- Excellent written and verbal communication skills.
All positions may require participation in video and in\-person interviews as part of the hiring process. All candidates will be evaluated based on job\-related competencies, and all candidates’ privacy rights and data security will be protected in accordance with applicable laws.
We are committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job\-related reasons regardless of an applicant's race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, marital status, genetic information, protected veteran status, or any other status protected by law.
Eaton believes in second chance employment. Qualified applicants with arrest or conviction history will be considered regardless of their arrest or conviction history, consistent with the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act and other local laws.
You do not need to disclose your conviction history or participate in a background check until a conditional job offer is made to you. After making a conditional offer and running a background check, if Eaton is concerned about conviction that is directly related to the job, you will be given the chance to explain the circumstances surrounding the conviction, provide mitigating evidence, or challenge the accuracy of the background report.
To request a disability\-related reasonable accommodation to assist you in your job search, application, or interview process, please call us at 1\-800\-836\-6345 to discuss your specific need. Only accommodation requests will be accepted by this phone number.
We know that good benefit programs are important to employees and their families. Eaton provides various Health and Welfare benefits as well as Retirement benefits, and several programs that provide for paid and unpaid time away from work. Click here for more detail: Eaton Benefits Overview. Please note that specific programs and options available to an employee may depend on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements.
Salary Context
This $122K-$179K 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 Eaton, 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. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $122K to $179K.
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.
Eaton AI Hiring
Eaton has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Golden, CO, US. Compensation range: $179K - $179K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.