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About This Role
AI is exploding. Our products are everywhere. We’re rewriting the rules on how local businesses sell, advertise, and win using EZ\-AD TV, Dentaloo, and EZ\-Commerce. From barbershops to Walmart, we’re the engine behind screens, software, and marketing that just works. If you live for building with bleeding\-edge AI and can prompt circles around the competition, keep reading. Only A\-players. Only builders. Only now.
We have momentum you can feel. 30,000\+ units sold. Seven countries. Big brands, retail chains, and tens of thousands of small business owners using our platforms every day. You’re not joining a sleepy company, you’re joining a rocketship. The only thing growing faster than our user base is the demand for automations, integrations, and rapid\-fire innovation. This is your chance to be the AI expert other devs turn to.
Claude code is our next unlock. You’ll architect, prompt, and build workflows that make our tech smarter, faster, and easier for the end user. Think: Zapier meets ChatGPT, but for the real world of screens, sales, and retail. If you can show us what’s possible, the upside is massive. Get in early, build things that last, and shape the future of local business automation.
WHY THIS JOB IS DIFFERENT
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- No pointless meetings, no slow committees. We move fast and ship faster.
- You’ll work on real products, not demos or vaporware. What you build will be used by thousands within weeks.
- Your prompts, code, and workflow designs will power everything from digital signage to e\-commerce automation. If you have ideas, we want them implemented.
- This is not a code\-only cave job. You’ll talk to users, test with real data, and see the impact of your work instantly.
- We’re only hiring one or two experts at this level. Join at the ground floor and define how we do AI, forever.
WHAT YOU’LL ACTUALLY DO
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- Design, test, and deploy advanced Claude code prompts and workflows that automate and enhance our products (EZ\-AD TV, Dentaloo, EZ\-Commerce).
- Prototype, iterate, and refine AI\-driven solutions for both internal teams and external customers.
- Work closely with product, sales, and support to translate real\-world user needs into robust automations and tools.
- Document, teach, and evangelize best practices for prompt engineering and workflow design, make others better.
- Jump into live troubleshooting and rapid iteration as needed. You’re available, agile, and flexible when the work calls for it.
- Act as our internal go\-to expert for all things Claude code and code X. If it can be automated, you’ll find a way.
WHAT WE’RE LOOKING FOR
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- Expert\-level skill with Claude code and related AI prompt engineering (show us a portfolio: personal, professional, or both).
- Proven ability to design, test, and optimize AI\-driven workflows, not just toy examples but real, working solutions.
- Natural communicator. You can explain complex logic to non\-technical teammates and inspire users with what’s possible.
- Flexible, responsive, and hungry. You like solving new problems every day. You don’t wait for instructions, you create your own roadmap.
- Deep understanding of user workflows and the “why” behind every automation. You think like a user, not just a coder.
- Passion for the future of AI and excitement about changing how businesses work at scale.
- Availability throughout the day to jump on calls, test, and collaborate. We move fast and expect the same.
WHAT YOU GET
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- Early, high\-impact seat on a team that’s changing entire industries. What you build will be used by thousands, fast.
- Strong base compensation plus performance upside. Let’s talk specifics based on your level and impact.
- Remote flexibility. As long as you’re available and communicative, we don’t care where you sit.
- Direct access to founders, leadership, and decision\-makers. Your work shapes our roadmap.
- Resources to test, break, and build with the best AI tools available.
- A team of A\-players who will push you to do your best work, and have fun while doing it.
This is not a corporate job. This is a movement. If you’re the best Claude code engineer in every room you walk into, show us your portfolio and let’s build the future together.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EZ-AD TV, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EZ-AD TV AI Hiring
EZ-AD TV has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Troy, MI, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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