Interested in this AI/ML Engineer role at Industrial Electric Manufacturing?
Apply Now →About This Role
At IEM, we're not just building innovative electrical distribution systems, we're shaping the future. IEM is dedicated to delivering world\-class solutions for complex power needs. After 75 years, we continue to push the boundaries of what's possible. Whether you're an experienced professional or just starting out, you'll have the opportunity to contribute, grow, and make a lasting impact on industries that power the world's most dynamic markets.
Position Summary
The Lead AI Development Engineer will design and build AI\-driven engineering applications that support IEM's Order, Applications, and Design Engineering functions. Reporting to the VP of Engineering, this role focuses on the core development of agentic AI applications, generative tools, and automated wiring systems that increase engineering efficiency, with occasional travel to support deployment and stakeholder alignment.
Ideal Candidate Profile
The ideal candidate brings 10\-15 years of experience in Order, Applications, or Design Engineering, with strong AutoCAD, SolidWorks, and BOM development skills, paired with hands\-on experience building agentic AI applications and familiarity with commercial LLMs. They are motivated by solving complex engineering problems through automation and enjoy building tools from the ground up.
Key Responsibilities
- Design, develop, and maintain agentic AI applications that support Order, Applications, and Design Engineering
- Build automated wiring system tools and generative applications that increase engineering efficiency
- Leverage commercial LLM platforms to develop AI\-powered engineering solutions
- Collaborate with Engineering stakeholders to translate BOM, AutoCAD, and SolidWorks workflows into automated processes
- Test, iterate, and refine AI applications based on engineering team feedback
- Partner with the VP of Engineering to prioritize the AI development roadmap
Qualifications
- 10\-15 years of experience in Order Engineering, Applications Engineering, or Design Engineering
- Proficiency in AutoCAD and SolidWorks
- Demonstrated experience creating and managing Bills of Materials (BOMs)
- Experience developing agentic AI applications
- Familiarity with commercial LLM platforms
- Ability to travel up to 10% as needed
Workplace Conditions
- Fully remote position with limited travel required
- Reports directly to the VP of Engineering
- Collaborates with Engineering, IT, and cross\-functional teams
- Travel required: approximately 10%
- Standard home\-office environment
Physical Demands
- Prolonged periods of sitting and working at a computer
- Occasional travel for meetings or site visits
- Ability to navigate engineering or manufacturing environments as needed
- Occasional lifting of equipment (e.g., laptop) up to 15 lbs
Why Join IEM
At IEM, you'll join a team that powers some of the world's most ambitious projects. We're engineers, makers, and problem\-solvers who thrive on tackling complex challenges and delivering solutions that keep industries moving forward. If you're driven, collaborative, and ready to make an impact, we'd love to hear from you. Your creativity and passion can help us achieve great things—come be part of the journey.
Learn more about IEM at https://www.iemfg.com
We offer comprehensive and competitive benefits package designed to support our employees' well\-being, growth, and long\-term success. View a snapshot of our benefits at https://www.iemfg.com/careers
Recruiting Scams
Beware of recruiting scams. IEM never charges candidates fees, and all recruiter emails come from an @iemfg.com address. If you suspect fraudulent activity, do not share personal information and report it to us at iemfg.com/contact
Non\-Discrimination Statement
IEM does not discriminate against any applicant based on any characteristic protected by law.
Privacy
Information collected and processed as part of your IEM Careers profile, and any job applications you choose to submit is subject to IEM's Workforce Member Privacy Policy.
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 Industrial Electric Manufacturing, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Industrial Electric Manufacturing AI Hiring
Industrial Electric Manufacturing has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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.