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About This Role
Itron is innovating new ways for utilities and cities to manage energy and water. We create a more resourceful world to protect essential resources for today and tomorrow. Join us.
The AI Manufacturing Engineer designs, develops, deploys, and supports AI-enabled solutions that enhance manufacturing processes, test systems, product quality, and factory operations. This role combines expertise in industrial engineering, data science, machine learning, software development, and manufacturing systems to solve complex operational challenges in a technology-driven Utilities & Energy environment.
The ideal candidate is passionate about applying artificial intelligence technologies to real-world manufacturing environments and can translate business needs into practical, scalable solutions that improve operational performance, customer outcomes, and business value. The successful candidate demonstrates a customer-centric mindset, accountability for results, and the ability to collaborate effectively across cross-functional teams.
Duties & Responsibilities
- Develop, train, validate, and deploy machine learning models for manufacturing applications.
- Build predictive analytics solutions that improve product quality, throughput, equipment reliability, process optimization, and customer outcomes.
- Design AI-based systems for anomaly detection, defect classification, root-cause analysis, and predictive maintenance.
- Develop computer vision solutions for automated inspection, quality assurance, and manufacturing process verification.
- Collaborate with manufacturing, quality, operations, and test engineering subject matter experts to identify high-value AI use cases and prioritize implementation opportunities.
- Support Industry 4.0 initiatives through the deployment of AI-enabled manufacturing solutions that drive operational excellence and innovation.
- Integrate AI applications with Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP) platforms, Product Lifecycle Management (PLM) systems, manufacturing databases, test systems, and production equipment.
- Create dashboards, monitoring tools, and decision-support systems that transform manufacturing data into actionable insights.
- Implement digital twins and process simulation capabilities to improve manufacturing efficiency and decision-making.
- Develop scalable data pipelines for structured and unstructured manufacturing data from production, quality, and operational systems.
- Ensure model accuracy, reproducibility, data integrity, cybersecurity requirements, and regulatory compliance.
- Establish and maintain best practices for AI governance, model monitoring, lifecycle management, and responsible AI deployment.
- Partner with Manufacturing Engineering teams to improve production yields, cycle times, Overall Equipment Effectiveness (OEE), capacity utilization, and process stability.
- Support New Product Introduction (NPI) activities through data-driven manufacturing analytics and process optimization.
- Collaborate with Test Engineering teams to improve automated test coverage, diagnostic capability, and test efficiency.
- Participate in continuous improvement initiatives using AI-driven insights and data-informed decision making.
- Work cross-functionally with Operations, Quality, Product Development, Supply Chain, Information Technology, and other stakeholders to deliver business results.
- Communicate technical findings, recommendations, and project outcomes effectively to both technical and non-technical audiences.
- Provide guidance, training, and mentorship on AI technologies, advanced analytics, and data-driven decision-making practices.
- Stay current on emerging AI technologies, manufacturing innovations, industry trends, and best practices.
- Demonstrate accountability for project execution, deliverables, and measurable business impact while contributing to a culture of agility, collaboration, authenticity, and innovation.
Required Skills & Experience
- Bachelor’s degree in Computer Science, Electrical Engineering, Industrial Engineering, Data Science, Manufacturing Engineering, or a related technical field.
- Experience developing solutions using Python or similar programming languages.
- Experience with MLOps, model deployment, monitoring, and lifecycle management.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-Learn, or equivalent technologies.
- Experience with data analysis, statistics, predictive modeling, and advanced analytics techniques.
- Experience working with SQL databases, data processing tools, and large-scale datasets.
- Understanding of manufacturing processes, industrial automation, quality systems, operational environments, or related production systems.
- Experience in manufacturing engineering, industrial engineering, test engineering, quality engineering, operations engineering, or related disciplines.
- Experience implementing AI agents and intelligent automation solutions for manufacturing and quality process improvements.
- Experience developing dashboards, reporting tools, and data visualization solutions.
- Strong problem-solving, analytical thinking, and root-cause investigation skills.
- Ability to communicate complex technical concepts clearly and effectively to diverse audiences.
- Ability to work collaboratively across multiple functions, teams, and geographic locations.
- Strong project management, execution, and organizational skills.
- Demonstrated ability to deliver customer-focused solutions that drive operational improvements and business results.
- Commitment to continuous learning, innovation, accountability, and inclusive collaboration.
Preferred Skills & Experience
- Experience with computer vision, industrial image processing, and automated inspection technologies.
- Experience with cloud platforms such as Microsoft Azure and cloud-based AI services.
- Knowledge of Industrial Internet of Things (IIoT), MES, SCADA, PLCs, industrial control systems, and manufacturing data platforms.
- Experience deploying AI and machine learning solutions in production manufacturing environments.
- Familiarity with Statistical Process Control (SPC), Six Sigma, Lean Manufacturing, Kaizen, or continuous improvement methodologies.
- Knowledge of electronics manufacturing, utility products, smart metering technologies, test systems, industrial equipment, or energy-focused manufacturing operations.
- Experience working with digital twin technologies, simulation tools, or advanced manufacturing analytics platforms.
- Familiarity with data governance, responsible AI practices, cybersecurity principles, and regulatory compliance requirements.
- Experience supporting smart factory, connected factory, or Industry 4.0 transformation initiatives.
- Advanced degree in a relevant engineering, computer science, data science, or analytics discipline.
Benefits Info: This position also includes a competitive benefit package including; financial, social, health and wellbeing programs, paid vacation, 401k matching, employee stock purchase program, hybrid work schedule, and more!
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Itron is committed to building an inclusive and diverse workforce and providing an authentic workplace experience for all employees. If you are excited about this role but your past experiences don't perfectly align with every requirement, we encourage you to apply anyway. In the end, you may be just who we are looking for!
The successful candidate's starting wage will be determined based on permissible, non-discriminatory factors such as skills and experience.
Itron is proud to be an Equal Opportunity Employer. If you require an accommodation to apply, please contact a recruiting representative at 1-800-635-5461 or email [email protected].
Itron is transforming how the world manages energy, water and city services. Our trusted intelligent infrastructure solutions help utilities and cities improve efficiency, build resilience and deliver safe, reliable and affordable service. With edge intelligence, we connect people, data insights and devices so communities can better manage the essential resources they rely on to live. Join us as we create a more resourceful world: www.itron.com
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Itron, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Itron AI Hiring
Itron has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span West Union, SC, US, Liberty Lake, WA, US. Compensation range: $91K - $91K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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