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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.
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 Distributed Intelligence Data Science team is seeking a motivated and curious 2027 Summer Data Science Intern to help advance innovation in the Utilities & Energy sector. Distributed Intelligence helps utilities achieve greater distribution system visibility by combining granular grid-edge data with an open enterprise application platform. In this role, you will collaborate with experienced data scientists, software engineers, and business stakeholders to support solutions that improve grid optimization, asset management, distributed energy resource (DER) integration, and customer engagement.
We welcome candidates from diverse backgrounds who are excited to learn, contribute, and grow in a collaborative, customer-centric environment.
Internship Details
Duration:
- 40 hours per week for 10 weeks during Summer 2027
Target Start Date:
- May/June 2027
End Date:
- August/September 2027, depending on academic schedule
Location:
- Liberty Lake, Washington
Duties & Responsibilities:
- Support the development, maintenance, and enhancement of data and machine learning pipelines used in production environments.
- Assist with the implementation and maintenance of cloud-based infrastructure while demonstrating accountability and attention to quality.
- Clean, prepare, and validate moderate- to large-scale datasets (tens to hundreds of gigabytes) for analysis, model development, and operational use.
- Partner with senior team members and subject matter experts to conduct exploratory data analysis (EDA) and communicate insights clearly to technical and non-technical audiences.
- Contribute to research, testing, and improvement of machine learning models under the guidance of experienced data scientists.
- Collaborate effectively with data scientists, software engineers, product managers, and other stakeholders to deliver innovative and customer-focused solutions.
- Demonstrate an agile mindset by adapting to evolving priorities, incorporating feedback, and continuously improving work outputs.
- Uphold authentic and inclusive collaboration practices that value diverse perspectives and ideas.
- Support projects that help utilities improve operational efficiency, reliability, and customer outcomes.
Required Skills & Experience:
- Currently enrolled in an accredited bachelor's degree program in a related field such as Statistics, Data Science, Applied Mathematics, Computer Science, Software Engineering, or a similar discipline.
- Completion of at least two years of college coursework by the internship start date.
- Experience using AI agent-driven tools to support software development, testing, code review, or related technical activities.
- Intermediate proficiency in Python and SQL.
- Knowledge of statistical methods, data analysis techniques, and data-driven problem solving.
- Exposure to machine learning through academic coursework, independent projects, research, internships, or professional experience.
- Strong written and verbal communication skills with the ability to share ideas clearly and respectfully.
- Demonstrated ability to work collaboratively in team environments while maintaining accountability for assigned work.
- Interest in developing innovative solutions that address customer and business needs within the Utilities & Energy industry.
Preferred Skills & Experience:
- Previous internship, research, or professional experience related to data science, analytics, software engineering, or machine learning.
- Experience using AI agent-supported tools for interactive data analysis and technical problem solving.
- Familiarity with cloud platforms such as AWS or Microsoft Azure.
- Experience with version control systems such as GitHub.
- Enrollment in a master's degree program in a STEM-related discipline, including Statistics, Data Science, Software Engineering, Computer Science, Applied Mathematics, or a similar field.
- Experience working on collaborative technical projects involving cross-functional teams.
- Interest in electric utility operations, grid modernization, distributed energy resources, or related energy technologies.
Physical Demands:
- This is a typical office-based position with no unusual physical requirements.
- The role is associated with the Liberty Lake, Washington location.
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!
The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills and experience and may vary by location. Liberty Lake, WA: $15.87 - $44.23 per hour
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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
Salary Context
This $31K-$91K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 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. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($61K) sits 71% below the category median. Disclosed range: $31K to $91K.
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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