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About This Role
Overview
We’re looking for a high\-level funnel builder who understands both conversion and design.
This is not a role for someone who simply knows how to drag and drop sections into a page builder. We’re looking for someone who understands why a funnel is structured the way it is, how a landing page should flow, how direct\-response design differs from traditional web design, and how to turn an offer into a page that looks polished and is built to convert.
You should have a strong eye for design, a solid understanding of direct\-response marketing, and experience using modern AI tools such as Claude and/or Codex to dramatically accelerate the way you design and build pages.
The right person knows how to use AI as a tool without producing something that looks AI\-generated.
Key Responsibilities
- Designing and building landing pages, sales pages, opt\-in pages, webinar funnels, checkout flows, upsell pages, and other funnel assets.
- Taking an offer, wireframe, concept, or marketing direction and turning it into a finished, polished funnel.
- Building inside platforms such as GoHighLevel, ClickFunnels, and similar funnel/page\-building platforms.
- Using Claude, Codex, and other AI tools to accelerate page creation, development, design exploration, and iteration.
- Creating custom HTML/CSS/JS where necessary to push platforms beyond their standard templates and components.
- Translating direct\-response marketing principles into page structure, visual hierarchy, layouts, and user experience.
- Working with headlines, copy, sections, proof, offers, CTAs, FAQs, pricing, bonuses, and other conversion elements to create a cohesive page.
- Iterating rapidly based on feedback while maintaining a high visual and technical standard.
- Ensuring pages look great across desktop and mobile.
- Improving existing funnels rather than simply rebuilding them.
- Working closely with marketing, copy, media, and strategy teams to turn ideas into production\-ready funnel experiences.
Qualifications
- Funnel Knowledge: You should have a strong understanding of how the individual pages work together as a system, not just how to build each page.
- Direct\-Response Design: We are specifically looking for someone who understands performance\-oriented, direct\-response web design. You should be comfortable designing pages that are bold, clear, high\-energy, and conversion\-driven rather than purely corporate or minimalist.
- Strong Design Eye: Extremely important is a very good instinct for design. We do not want someone who needs a designer to tell them every pixel to move. You should be able to look at a page and immediately recognize when something feels off, then know how to improve it.
- AI\-Assisted Building: AI should already be part of your workflow. We are especially interested in people who have learned how to use Claude and/or Codex to create production\-quality landing pages and custom web experiences quickly. You do not need to be an AI engineer. AI is expected to help you move faster, but you are still responsible for the final product. You should be highly comfortable using these tools Claude, Claude Code, Codex, ChatGPT, AI\-assisted IDEs or development environments and AI image or creative tools where appropriate.
- Platform \& Technical Experience: You do not have to be a traditional full\-stack developer, but you should be technically capable enough to get beyond the limitations of a page builder when necessary with strong experience with one or more of the following is preferred. GoHighLevel, ClickFunnels, WordPress, Webflow, Framer, Shopify and similar page or funnel builders.
10X TOTAL REWARDS
We offer a comprehensive benefits package for full\-time employees that includes:
- Medical, dental, and vision for employees and their dependents
- Paid Time Off policy that increases based on tenure with the company
- Employee Assistance Program through Mutual of Omaha
- 401k with company match
- Pet Insurance through MetLife for your 10X pets
- Company Paid Employee wellness initiatives through BeyondMed
- Professional Development through Continued Education: we provide team members complete access to our range of educational resources valued at over $250,000 in areas such as Sales, Operations, People, Finance and Marketing
- Uncapped Commission Potential: all team members have the opportunity to sell our Products/Services (and are trained on how to do so)
COMMITMENT TO DIVERSITY
As an equal opportunity employer committed to meeting the needs of a multigenerational and multicultural workforce Grant Cardone Enterprises recognizes that a diverse staff, reflective of our community, is an integral and welcome part of a successful and ethical business. We hire local talent at all levels regardless of race, color, religion, age, national origin, gender, gender identity, sexual orientation or disability, and actively foster inclusion in all forms both within our company and across interactions with clients, candidates and partners.
Salary Context
This $75K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Grant Cardone Enterprises, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($85K) sits 60% below the category median. Disclosed range: $75K to $95K.
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.
Grant Cardone Enterprises AI Hiring
Grant Cardone Enterprises has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Aventura, FL, US. Compensation range: $95K - $95K.
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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