Interested in this AI/ML Engineer role at Paxton Automotive Marketing?
Apply Now →About This Role
About Paxton Marketing
Paxton Marketing is a fast\-growing digital marketing and technology company focused on delivering innovative solutions for businesses across multiple industries, including automotive dealerships. We combine cutting\-edge marketing strategies, custom technology development, and AI\-driven automation to help our clients operate more efficiently and grow their businesses.
Position Overview
We are seeking a highly motivated Web \& AI Solutions Specialist to join our team. This role combines web design, search engine optimization, AI system management, and custom technology development. The ideal candidate is both creative and technical, with a passion for improving business processes through automation and artificial intelligence.
Key Responsibilities
- Web Design \& Development
- Design, update, and maintain client websites
- Improve website performance, user experience, and functionality
- Implement website enhancements and troubleshoot technical issues
- Collaborate with marketing teams to support campaign objectives
SEO Management
- Manage and execute monthly SEO strategies for multiple businesses
- Monitor search rankings, traffic, and performance metrics
- Perform keyword research and on\-page optimization
- Develop SEO strategies for automotive dealerships and other industries
- Generate performance reports and recommendations
AI Systems \& Automation
- Maintain and optimize existing AI platforms, programs, and workflows
- Develop new AI\-powered solutions to improve internal and client operations
- Integrate AI tools into business processes and software platforms
- Troubleshoot AI systems and ensure ongoing reliability
- Research emerging AI technologies and recommend improvements
Custom Platform Development
- Build and enhance proprietary business tools and platforms
- Create workflow automations that improve efficiency and scalability
- Work with APIs, integrations, and third\-party software solutions
- Identify opportunities to streamline operations through technology
Qualifications
- Experience with website design and development
- Strong understanding of SEO best practices and analytics
- Experience working with AI tools, automation platforms, or workflow systems
- Ability to manage multiple projects and deadlines simultaneously
- Strong problem\-solving and communication skills
- Self\-motivated with a desire to continuously learn new technologies
Preferred Qualifications
- Graphic design experience (strongly preferred)
- Experience working with automotive dealership marketing
- Familiarity with AI integrations, APIs, and automation tools
- Experience with WordPress, web hosting, and website maintenance
- Knowledge of modern no\-code and low\-code development platforms
What We Offer
- Opportunity to work with innovative AI technologies
- Growth\-focused and entrepreneurial work environment
- Ability to make a direct impact on client success and company growth
- Flexible and collaborative team culture
Pay: $60,000\.00 \- $70,000\.00 per year
Benefits:
- Paid time off
Application Question(s):
- Explain your experience with SEO. What specific strategies have you implemented to improve rankings and organic traffic, and what results did you achieve?
- Tell us about a workflow, process, or business operation you have improved using AI or automation. What problem were you solving, and what tools did you use?
- What AI tools, platforms, or technologies do you use regularly today, and how have you applied them in a professional setting?
- If you were hired, what opportunities do you see for AI and automation to improve the efficiency of a marketing agency like Paxton Marketing?
- Please provide links to any websites, software projects, automations, designs, or portfolios that best demonstrate your experience.
Ability to Commute:
- Fernandina Beach, FL 32034 (Required)
Work Location: In person
Salary Context
This $60K-$70K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Paxton Automotive Marketing, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($65K) sits 64% below the category median. Disclosed range: $60K to $70K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Paxton Automotive Marketing AI Hiring
Paxton Automotive Marketing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fernandina Beach, FL, US. Compensation range: $70K - $70K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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