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Location: Downers Grove, IL (Chicagoland Area — Local Candidates Preferred)
Job Type: Contract / 1099 \| Part\-Time to Full\-Time
Compensation: 100% Commission — 20–30% of every deal you close
WHAT THIS IS — AND WHAT IT ISN'T
This is a 100% commission role. There is no base salary.
We know that's not for everyone — and that's intentional. We're looking for a specific type of person: someone who knows they can sell, doesn't want a ceiling on their income, and doesn't need someone else to hand them leads.
In exchange for no base, you get something most sales jobs don't offer:
- 20–30% commission on every deal you close — significantly higher than what you'd earn in a salaried role
- No territory restrictions — the entire Chicagoland market and beyond is yours
- No quota pressure from above — you run your own business within ours
- A product that sells itself to the right buyers — local businesses are actively looking for what we offer
The honest math: A salaried sales rep earning $60K with a 5–8% commission needs to close $750K\+ in revenue to match what you'd earn here closing $250K at 25%. You keep more of what you produce.
WHAT YOU CAN EARN
Our AI services range from $1,500 setup projects to $5,000\+ custom automation builds. Here's what your commission looks like at different activity levels:
- 2 deals/month \| Avg $2,500 contract → $1,250–$1,500/month → \~$15K–$18K/year
- 4 deals/month \| Avg $3,500 contract → $3,500–$4,200/month → \~$42K–$50K/year
- 6 deals/month \| Avg $4,000 contract → $6,000–$7,200/month → \~$72K–$86K/year
- 8 deals/month \| Avg $4,500 contract → $9,000–$10,800/month → \~$108K–$130K/year
- 10\+ deals/month \| Avg $5,000\+ contract → $12,500–$15,000\+/month → $150K\+/year
Note: These figures are based on one\-time setup fees only. Many clients add monthly retainers after the initial build — those are additional commission opportunities.
WHAT YOU'LL BE SELLING
You're not selling software subscriptions or ad packages. You're selling outcomes — and businesses feel the results immediately.
Sales advantage: Most of your prospects have never seen a live AI agent respond to a customer inquiry in real time.
YOUR IDEAL PROSPECTS
You'll be selling to local and mid\-size business owners across Chicagoland and the surrounding suburbs — decision\-makers who are already spending on marketing but haven't tapped into AI yet.
Best\-fit industries:
- Restaurants, cafes, and hospitality
- Medical and dental practices
- Law firms and professional services
- Real estate agents and brokerages
- Home service businesses (HVAC, plumbing, roofing, landscaping)
- Retail shops and local e\-commerce
- All B2B businesses
If there is a workflow to follow we can easily automate that process to work on its own 24/7\. If they are looking to hire any employees we can most likely create an AI employee who can do that job.
These are not enterprise deals with 6\-month sales cycles. Most decisions are made by one person — the owner — and deals can close in a single conversation when the fit is right.
WHO WE'RE LOOKING FOR
You're a strong fit if:
- You have 2\+ years of B2B or B2C sales experience with a track record of closing, not just prospecting
- You're comfortable with outbound — cold calls, LinkedIn outreach, walking into a business — whatever it takes
- You can explain technology to non\-technical people without making them feel dumb
- You're self\-motivated and don't need a manager standing over you to make calls
- You're connected in the local business community — Chicagoland, suburbs, BNI, chamber of commerce
- You're curious about AI — you don't need to build it, but you need to be excited to talk about it
You're not a fit if:
- You need a guaranteed paycheck to feel secure — there is no base here
- You expect the company to generate your leads for you
- You've never sold anything consultatively — this isn't a transactional product
WHAT WE PROVIDE
No base doesn't mean no support. Here's what you get from us:
- Full product training — everything you need to demo and sell confidently
- Sales collateral — one\-pagers, case study frameworks, and a demo script
- Live demo access — show prospects a working AI agent in real time during your sales call
- Fast turnaround — deals are scoped and delivered quickly so your clients stay happy and refer others
- Flexible schedule — you work when and how you want, as long as you're producing
*Must be local to the Chicagoland area. This is an independent contractor / 1099 position which can lead to a part\-time or full\-time position. Creative Ground LLC is an equal opportunity employer.*
Pay: $18,000\.00 \- $150,000\.00 per year
Benefits:
- Flexible schedule
- Work from home
Work Location: Remote
Salary Context
This $18K-$150K range is in the lower quartile 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 Ash and Oak, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($84K) sits 62% below the category median. Disclosed range: $18K to $150K.
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.
Ash and Oak AI Hiring
Ash and Oak has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Chicago, IL, US. Compensation range: $57K - $150K.
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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