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About This Role
Who We Are
Arcadia is the AI\-powered energy intelligence platform for businesses. We replace fragmented tools and manual workflows with one platform to pay utility bills, buy energy, and advance sustainability — across every location, at enterprise scale.
Trusted by Fortune 2000 companies, Arcadia combines unified data, AI\-powered analytics, and expert advisory to help enterprise teams save money, mitigate risk, and cut carbon.
We deliver this through three comprehensive solutions:
- Utility Bill Management: Automating the entire utility bill lifecycle — from data capture and validation to payment processing and auditing.
- Energy Procurement Advisory: Bringing together comprehensive data, AI\-powered analytics, market expertise, and a strong partner network to make sophisticated procurement options accessible to all. .
- Sustainability Reporting — Verified emissions data with seamless integration into leading sustainability platforms.
Tackling the world's most complex energy challenges requires diverse thinking. We're building teams of people from different backgrounds, industries, and disciplines — united by a belief that energy management should be simple, intelligent, and a genuine driver of business value.
What we're looking for:
Most companies adopt AI by bolting it onto how they already work. The teams that pull ahead go further: they rethink the workflow itself, so people spend less time on repetitive work and more on the problems that actually need them. AI is already gaining momentum across Arcadia. This role exists to make it take hold: to turn early wins into scaled, lasting change.
The AI Operations Lead is the operating layer of Arcadia's AI transformation. You will work directly with teams across the company, in product, engineering, go\-to\-market, finance, and operations, to learn how the work actually happens and bring AI into it. In practice that means designing and shipping the Claude skills, agents, and workflows teams adopt, and establishing the patterns, standards, and champion network that let those wins compound long after you have moved to the next team. The role reports into R\&D Operations, the function that already connects these teams, and carries a mandate that spans all of them.
Outcomes are the bar. An AI workflow earns its place by what it changes for the team, not by how novel it is, and that standard decides what scales and what gets retired. You will hold the work to that test: adoption that holds, time given back to people for higher\-value work, and workflows that outlast the team where they started. You are as fluent in designing a Claude skill as you are mapping a messy process or bringing a skeptical team along, and you solve a problem once, turn it into a pattern, and hand it to the rest of the company.
Arcadia is open to fully remote candidates and employees have access to co\-working spaces.
\#LI\-REMOTE
What you'll do:
- Embed team to team. Rotate through teams across the company to map current\-state workflows end to end: where time goes, where decisions stall, where handoffs between teams and functions compress or disappear. Translate that into a prioritized set of high\-leverage AI opportunities for each team.
- Define and build Claude skills and workflows. Design, build, and ship reusable Claude skills, agents, and automations that teams actually adopt, not demos. Redesign the workflow around the capability and outcome rather than bolting AI onto an unchanged process.
- Engage and grow the champion network. Every team is expected to have an AI champion. In many cases you'll help identify who that should be, then enable them to extend, maintain, and evangelize workflows after you've moved on, so capability compounds instead of depending on you.
- Codify what works. Turn every win into a reusable pattern (a shared library of skills, templates, and playbooks) so a solution built for one team becomes a starting point for the next.
- Keep builds safe and compliant. Partner with the governance lanes (Security \& Compliance; AI Technical Approach) so every workflow meets Arcadia's data\-handling, classification, and human\-in\-the\-loop standards by design.
- Measure and report impact. Track adoption, time saved, and workflow outcomes. Make the case for scaling what works and retiring what doesn't, and feed signal back to leadership.
What Success Looks Like (First Two Quarters):
- A repeatable engagement model for embedding with a team, mapping its workflow, and shipping its first production AI workflow.
- A growing, self\-sustaining champion network across the company, with champions independently maintaining and extending what's been built.
- A shared skill\-and\-workflow library that measurably shortens the time to stand up the next team.
- Documented adoption and time\-saved outcomes that move teams out of the pilot loop and into scaled use.
Must\-haves:
- You bring 5–8 years spotting where workflows break down across departments, then working hand\-in\-hand with engineering to ship solutions: automations, AI workflows, internal tools that teams actually adopted, not just launched. Extra credit if you've done the building yourself.
- Strong workflow\- and process\-design instincts. You can sit with a team in any function, see the real operating model underneath the org chart, and redesign it.
- The interpersonal range to win trust across very different teams and cultures, and to bring a skeptic along.
- A bias for reusable systems over heroics. You'd rather build the pattern once than solve the same problem ten times.
- Comfort operating in ambiguity inside a fast\-moving, post\-acquisition organization.
Nice\-to\-haves:
- Experience standing up enablement, center\-of\-excellence, or champion\-network models.
- Familiarity with AI governance concepts (data classification, human\-in\-the\-loop, security review).
- Exposure to AI\-native operating models at high\-performing software organizations.
- Background in R\&D, product, or engineering operations.
Benefits:
- "Remote first" culture \- work anywhere in the US as long as you have a reliable internet connection
- Flexible PTO \- no accrued hours and no limit on the number of vacation days exempt employees can take each year
- 11 annual holidays
- 10 days sick leave
- Up to 3 weeks bereavement leave
- Up to 4 weeks of caregiver leave
- Military leave for eligible services or events
- 2 volunteer days off
- 2 professional development days off
- 10 weeks paid parental bonding leave for *all* parents and additional medical recovery time for eligible employees
- 75\-95% employer cost coverage for medical, dental, and vision benefits for employees and dependents
Celebrating Diversity and Inclusion
Here at Arcadia, we cultivate diversity, celebrate individuality, and believe unique perspectives are key to our collective success in creating a clean energy future. Arcadia is committed to equal employment opportunities regardless of race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, protected veteran status, or any status protected by applicable federal, state, or local law.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation by emailing [email protected] prior to completing your application.
Target Annual Compensation Range for this role will be $127,500\- $228,400\. There will also be a competitive benefits and equity (bonus if applicable) component to the package. The exact compensation at which this job is filled will be determined by the skills, experience, and location of the qualified candidate. Please note that we are unable to offer visa sponsorship for this position at this time.
Automated Screening: Arcadia uses AI\-assisted technology to evaluate candidates on relevant skills, experience, and education as outlined in the job description. Human review is part of all hiring decisions. Candidates may request an alternative selection process by contacting [email protected] before submitting their application.
Data Privacy \& California Residents: You voluntarily provide personal information (such as your resume, contact details, and assessment responses) when submitting an application. We may use this to evaluate your candidacy and derive inferences from this data to match your profile with open roles. For California Residents: This collection is consistent with the CCPA. You have the right to request access to or deletion of your data by contacting [email protected].
Please note that we are unable to offer visa sponsorship for this position at this time
Thank you
Salary Context
This $127K-$228K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Arcadia, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($177K) sits 19% below the category median. Disclosed range: $127K to $228K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Arcadia AI Hiring
Arcadia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $228K - $228K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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