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About This Role
AI Operations Coordinator
Company: CABS HeatingAir Conditioning
Location: West Sacramento, CA (in office 4 days, 1 day from home)
Type: Full-time, hourly
Pay: $22 to $26 per hour, with raises tied to the skills you build
Schedule: Monday through Friday, flexible hours within our business hours (8 AM to 5 PM most of the year). No phone duty.
Wanted: someone who checks the AI's homework.
Love getting things done with AI, but never take its word for it? Keep reading. There is a short puzzle in the application, and you will know it when you see it.
Let's skip the corporate part. We are CABS HeatingAir Conditioning, a family-owned heating and air company in West Sacramento. This year we built something most small businesses have not: a working team of AI agents, each one set up for a part of the business, from accounting and HR to marketing, service, and safety.
Our operations lead runs projects through those agents every day. Now she needs someone to work alongside her. Someone sharp who can pick up a project she starts, use the same AI tools, and help carry it to the finish line while she stays focused on the big picture.
In a single week that might mean turning a messy vendor report into a one-page summary, building a new-hire checklist with an agent, and catching the AI's mistake before it reaches anyone. That is the job.
A few honest things first
- This is a fast-paced seat. On a normal day you might touch a marketing report, a payroll checklist, and a vendor follow-up before lunch. If you prefer one task done the same way every day, this may not be the right seat, and that is okay.
- AI makes mistakes, and a big part of this job is catching them. If you would rather take the tool's answer as is, this may not be the right seat, and that is okay.
- This is mostly in the office: four days a week in West Sacramento and one day from home. Learning happens fastest side by side.
- Starting pay is $22 per hour while you train. It climbs to $26 per hour as you take on more, and we spell out below exactly what earns each raise.
Now the good news
- No phone duty. You are not on the customer phone line. Your time goes to projects, start to finish.
- Flexible hours. Full-time, Monday through Friday. You and your lead set your daily schedule within our business hours.
- You inherit a full AI team on day one. The agents, their instructions, and the systems are already built. You learn to run them, not build them from scratch.
- We teach you. You do not need a degree. One year of work experience is enough, and you do not need to be a programmer. If you can explain a task clearly in plain English and check the result, we will teach you the rest.5. Your raises are tied to skills, not to waiting. Three clear steps, from $22 to $26 per hour, each one unlocked by work you can show.
- You learn how a real business runs from the inside: operations, marketing, payroll, and accounting.
- You work directly with our operations lead, not three layers down. Your ideas get heard directly, not passed up a chain.
- Variety. No two weeks look the same.
- Health, vision, and dental insurance after 60 days. Paid sick leave, birthday pay, and paid vacation.
Your first 90 days
- Days 1 to 30: you work side by side with our operations lead, learn the AI agents, and take over two recurring weekly tasks.
- Days 31 to 60: you run those on your own and start picking up projects in marketing and office operations.
- Days 61 to 90: you carry projects from start to finish and begin learning payroll prep and accounting workflows.
How the pay grows
- Step 1, Training: $22 per hour for your first 90 days.
- Step 2, Independent: $24 per hour once you run at least three recurring workflows on your own.
- Step 3, Finance-ready: $26 per hour once you run payroll prep and accounting workflows through the AI and your work passes review.
You and our operations lead review each step together, so you always know where you stand and what comes next. Each raise is based on that review, and the steps are a guide, not a promise of timing.
Who does well in this seat
- You lead with common sense. When something looks off, you stop and check before you send it.
- You love to learn. You have taught yourself something lately just because you wanted to know how it works.
- You already use AI tools like ChatGPT, Claude, or Gemini, and you are curious how far they can go.
- You finish things. A half-done project bothers you.
- You have a feel for how a business runs, whether from an office, supervising a shift at a restaurant or store, or running a business of your own.
- You keep private things private. You will see sensitive business information, and you treat it with care.
- You stay calm when priorities change at 2 PM.
We do not expect you to arrive knowing heating and air, or how to build AI agents. We expect common sense, curiosity, and follow-through. We can teach the rest.
What you must bring
To keep this fair and quick for everyone, we only consider applications that meet all of these:
- You can work full-time, Monday through Friday, within our business day, four days a week in our West Sacramento office and one from home.
- The $22 per hour starting rate works for you.
- You are legally authorized to work in the United States.
- You have at least 1 year in an office, admin, operations, or supervisor role, or running a business of your own.
- You use AI tools for real work today, not just for fun, and can describe a specific example.
- You answer every required application question, including a short AI check that takes about 10 minutes.
If that is not you yet, this may not be the right seat right now, and that is okay. We would rather tell you up front so you can spend your time where it fits.
The details
AI Operations Coordinator. Full-time, hourly, Monday through Friday, flexible hours within our business hours. No phone duty. $22 to $26 per hour, based on the three steps above. Four days a week in our West Sacramento office, one day from home.
How to apply
Answer every application question honestly. One more thing. So we know you read this far, begin your answer to the question about how you use AI tools for real work with the words "Check it first." We only review applications that include it. It is our small way of finding the people who pay attention.
If your application is a fit, we will invite you to a paid two-hour working session where you use AI on a realistic office task. It is a real-world task, not a quiz. We just want to see how you think.
Want a head start? It is optional, but we would love to hear from you. Add a link to a 60-second video or voice recording, or a few written lines, that tells us about something you taught yourself.
We cannot wait to meet you.
CABS HeatingAir Conditioning is an equal opportunity employer. We consider qualified applicants with arrest or conviction records in line with the California Fair Chance Act, and we provide reasonable accommodations in the hiring process on request.
Salary Context
This $45K-$54K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CABS Heating & Air Conditioning, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($49K) sits 76% below the category median. Disclosed range: $45K to $54K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
CABS Heating & Air Conditioning AI Hiring
CABS Heating & Air Conditioning has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in West Sacramento, CA, US. Compensation range: $54K - $54K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
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