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About This Role
ETAP empowers customers to make informed decisions throughout the life cycle of their projects with innovative software solutions for electrical systems. By applying ETAP solutions, customers experience continuous intelligence during design and engineering and into operations and maintenance using a unified electrical digital twin platform. ETAP supports customers in their digital transformation and sustainable energy transitions for a green and smart future, helping them to prioritize safety, maximize reliability, and stay resilient.
Our employees' passion for excellence, innovation, and customer satisfaction is our most\-prized resource. If you share that passion — and want to be part of a company that leads the energy transition towards a cleaner and more resilient world for future generations — we invite you to join us!
ETAP is committed to creating a diverse work environment and is proud to be an Equal Opportunity Employer.
Job Title: Electrical AI Developer
Job Location: Irvine, CA
Job Type: Full\-time / Hybrid
As an Electrical AI Developer at ETAP, you will play a key role in the development, enhancement, and deployment of ETAP’s AI\-based solutions. In this role, you will collaborate with cross\-functional teams including power systems engineers, data scientists, software developers, and UI specialists to design and implement intelligent assistance tools that support modeling, analysis, planning, protection, and operation of electrical power systems. Your contributions will directly enable utilities, industries, and infrastructure customers to optimize system reliability, integrate renewable energy, and enhance overall grid performance.
Key Responsibilities:
- Design, develop, and maintain AI\-driven features for ETAP’s electrical engineering applications.
- Collaborate with power system engineers to translate domain knowledge and analytical workflows into machine learning models and intelligent automation tools.
- Implement algorithms for system modeling, network optimization, fault analysis, system planning, energy management, and grid stability.
- Develop and integrate large language model (LLM)\-based assistants to enhance user experience and reduce engineering workload.
- Work with data teams to curate training datasets, perform model validation, and ensure accuracy and reliability of AI outputs.
- Optimize computational performance and ensure scalability of AI\-driven functionalities across ETAP product suites.
- Participate in code reviews, documentation, testing, and continuous improvement processes.
- Participate in the software development lifecycle, including spec, coding, testing, debugging, and documentation of new web based AI features and enhancements.
- Support troubleshooting, debugging, and release cycles for already deployed copilot features.
- Prepare detailed technical documentation, including user manuals, technical specifications, and application notes on AI.
- Ensure that AI algorithms and software solutions comply with relevant industry standards and regulatory requirements.
- Stay current with emerging technologies in AI, LLM, power systems, cloud and software development.
Required Qualifications:
- Master's or Ph.D. degree in Electrical Engineering, Power Systems, Computer Science, AI/ML, or related field.
- Strong understanding of electrical power systems (generation, transmission, distribution, industrial power).
- Hands\-on experience with Python, C\+\+/C\# or similar programming languages, and machine learning frameworks.
- Ability to work collaboratively in a multidisciplinary environment.
- Strong problem\-solving, analytical, and communication skills.
- Highly motivated, self\-managed, details\-oriented, and well\-organized
Preferred Qualifications:
- Knowledge of large language models (LLMs), natural language processing (NLP), or reinforcement learning.
- Familiarity with cloud\-based deployment environments.
- Prior experience in utility, industrial, or renewable energy system applications.
- Contribute to white papers, technical articles, patents and presentations to share knowledge with the industry and promote ETAP's AI capabilities.
- Experience working with ETAP software is an advantage.
Education and Experience:
- Master's or Ph.D. degree in Electrical Engineering, Power Systems, Computer Science, AI/ML, or related field.
- Typically, 1\-3 years of experience in power systems engineering or a related field, with a focus on AI and LLM.
Work Authorization Requirement
Candidates must be authorized to work in the United States on a permanent basis without the need for current or future visa sponsorship.
Salary Range: 65,000\.00 \- 108,000\.00 USD Annual
This pay range represents the minimum and maximum compensation that the position offers, and final compensation can vary within the range depending on work location, job experience, skills, and relevant educational attainment and/or training.ETAP requires all successful applicants to undergo and pass a comprehensive background check before they start employment. Background checks will be conducted in accordance with local laws and may, subject to those laws, include proof of educational attainment, employment history verification, proof of work authorization, criminal records, identity verification, credit check. Certain positions dealing with sensitive and/or third party personal data may involve additional background check criteria.
ETAP is an Equal Opportunity Employer. We are committed to being an exemplary employer with an inclusive culture, developing a workplace environment where all our employees are treated with dignity and respect. We value diversity and the expertise that people from different backgrounds bring to our business.
Come and join ETAP to create the transformative technology that enables our customers to engineer a better world.
Salary Context
This $65K-$108K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Etap, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($86K) sits 52% below the category median. Disclosed range: $65K to $108K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Etap AI Hiring
Etap has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $108K - $108K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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