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About This Role
ZING
AI Platform Head \& Product Management Lead
ServiceQUIK Inc. \| Denver Tech Center, CO \| Full\-time
About ZING
ZING is an AI\-native SaaS platform serving SMBs, such as home service businesses, across the US. We give small business owners, from plumbers and cleaners to electricians and landscapers, what enterprise companies take for granted: a professional website, local SEO, an AI receptionist that answers every call, and marketing automation that runs itself.
We are not a startup figuring out AI. We are a company already running on it:
- Cash flow positive and funded, with capital in the bank to scale
- Agentic processes enabled throughout the system: AI agents handle inbound calls, AR recovery, onboarding, QA, and commissions today, in production, at scale
- 50% of company processes automated in the last 12 months, and we are nowhere near done
- Currently doubling the team and scaling go\-to\-market aggressively
- Thousands of live customer subscriptions, growing every week
We build production software with AI at the core of how we work, not as a feature bolted on. The question we ask about every process is: why does a human do this?
The Role
This is not a strategy role. You will be the one doing the work.
You will own ZING's entire technical operation: our proprietary website builder (Pixel), our internal CRM and operations platform (ATLAS), our AI receptionist infrastructure (MAX), and the growing fleet of agents that run the business. You will design the agentic org chart the same way a traditional executive designs a human one: which functions get an agent, how agents escalate, how they are monitored, and where humans stay in the loop.
You will report directly to the CEO and work alongside a small, high\-output team. There is a path to CTO as the organization scales. The title, like everything else here, gets built.
What You Will Own
AI\-native product delivery and product management. Own the product roadmap and ship at AI speed across Pixel, ATLAS, and our automated site generation pipeline. You decide what gets built, in what order, and why, then you build it. You orchestrate AI coding tools and agents plus a lean human team to produce what would normally take an engineering department.
Agent architecture. Design, build, and operate the agent hierarchy that runs the company: support triage, billing recovery, onboarding, QA, competitive intelligence, and functions we have not invented yet. You define the org chart of an agentic company.
Business process systems. Understand how a digital services business actually makes money, then encode it. Onboarding, fulfillment, billing, support, and sales ops become systems, not headcount.
Technical operations. Infrastructure, reliability, security, vendor management, and compliance\-critical systems. Our stack: Railway, Supabase, Prisma, Stripe, Cloudflare R2 and Workers, Twilio. You keep it fast and cheap but always looking for innovative ways to drive the required business outcome.
Who You Are
Must\-haves:
- You have personally built and shipped production software using AI\-native workflows (Claude Code, Cursor, Codex and agent frameworks, or equivalent). Not experimented. Shipped.
- You have designed or operated multi\-agent systems in production, or you have automated significant business processes end to end
- You understand business operations well enough to see a workflow and immediately map it to a system
- You are hands\-on\-keyboard daily and want to stay that way
- You thrive with ambiguity, ownership, and speed
Strong signals:
- Founder or founding engineer background
- Experience with SMB services, agencies, or vertical SaaS
- You have opinions about agent orchestration formed by breaking things in production, not by reading threads
This role is not for you if you measure success by team size, you need a spec before you build, or you see AI tooling as something your reports use.
Compensation
$300K OTE (on\-target earnings), structured as:
- Competitive base salary
- Quarterly performance bonus tied to clear, achievable targets
- Equity via our ESOP
Why This Role Is Different
Most AI leadership roles are about presenting roadmaps. This one is about building the blueprint for how an AI\-native company actually operates, on top of a real business with real revenue and real distribution. The systems you build here will run the company. Few roles anywhere offer that.
Job Type: Full\-time
Pay: $180,000\.00 \- $300,000\.00 per year
Benefits:
- Dental insurance
- Health insurance
- Vision insurance
Work Location: In person
Salary Context
This $180K-$300K range is above the 75th percentile 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 ServiceQUIK Inc., 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 ($240K) sits 10% above the category median. Disclosed range: $180K to $300K.
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.
ServiceQUIK Inc. AI Hiring
ServiceQUIK Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $300K - $300K.
Location Context
AI roles in Denver pay a median of $201,050 across 48 tracked positions. That's 8% below the national 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 3,708 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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