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About This Role
Overview
(Executive Track) — Start at $75K, \-\- Path to $150K within 12\-18 months
\*\*Company:\*\* Foxtone Media
\*\*Location:\*\* Lubbock, TX (Onsite)
\*\*Job Type:\*\* Full\-time
\*\*Pay:\*\* $36,000 to $75,000 per year base (based on experience/talent) \+ performance bonus. Documented path to \~$150K total comp within 12–18 months.
\*\*Schedule:\*\* Monday to Friday, daytime hours
\*\*Work setting:\*\* In\-person
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\#\# Full Job Description
We're a Lubbock\-based marketing agency hiring our first dedicated \*\*AI Automation Engineer\*\* — and this is not a typical entry\-level job. You'll start at \*\*$75,000\*\* with a \*\*written, milestone\-based path to roughly double that ($150K total comp) within 12–18 months\*\*, growing into a Head of AI \& Automation leadership seat.
If you're a sharp builder who's tired of long corporate ladders and wants to own real systems from day one, read on.
\#\#\# What you'll do
You'll own the AI tooling, automation, and agent orchestration across the agency so our founder and senior team can stop touching API keys, config files, and repetitive workflows. You'll turn vague requests ("can we stop pulling these reports by hand?") into reliable, monitored production systems.
This is part engineer, part operator. You'll write code when you need to, but most of the value is in choosing the right tools, wiring them together securely, and shipping automations that save the business real time and money.
\*\*Core responsibilities:\*\*
\- \*\*AI stack \& tooling\*\* — Evaluate, select, and maintain our AI tools (Claude, ChatGPT, Gemini, open\-source models); own API integrations and vendor credentials; track and optimize spend.
\- \*\*Security \& access\*\* — Manage API keys and secrets (1Password, Doppler, AWS Secrets Manager); set role\-based access; audit data flows so client data goes only where it should.
\- \*\*Automation development\*\* — Build and maintain agent workflows (n8n, Make, Zapier, Pipedream, or code); write Python/TypeScript when no\-code can't do the job; set up monitoring and alerting; document everything.
\- \*\*Agent orchestration\*\* — Design the "org chart" of agents: who does what, how they hand off, where humans review; manage prompt versioning, model swaps, and rollbacks.
\- \*\*Founder \& team interface\*\* — Run a weekly automation roadmap review; translate operator requests into shipped systems; train the team on tools they can safely use.
\#\#\# Your first 90 days
\- \*\*Days 1–30:\*\* Audit recurring tasks across the business and deliver a documented automation roadmap. Set up secrets management and access controls.
\- \*\*Days 31–60:\*\* Ship your first two automations to production (reporting plus onboarding or lead qualification). Build the internal playbook for how the team requests automations.
\- \*\*Days 61–90:\*\* Stand up monitoring and the agent org chart, and demonstrate measurable impact.
\#\#\# The path from $75K to \~$150K
The promotion path is written down, milestone\-based, and reviewed quarterly — real, not a verbal promise. Hit the milestones faster and the title and pay move faster.
\- \*\*Months 0–3:\*\* AI Automation Engineer — $75K base \+ bonus
\- \*\*Months 4–9:\*\* Senior AI Automation Engineer — higher base \+ bonus
\- \*\*Months 10–18:\*\* Head of AI \& Automation / Director of AI Operations — \~$150K total comp \+ profit\-share consideration
By month 12–18 you're running a function end\-to\-end: managing a budget, owning a documented business outcome, sitting in on leadership strategy, and hiring your own first direct report.
\#\#\# What we're looking for
\- You've \*\*built something\*\* — a side project, a tool, a freelance automation, a startup. Proof you ship without being told to.
\- Comfortable in a terminal; you can read code without panicking (Git, basic Linux, Python or Node).
\- You've used LLM APIs (Anthropic, OpenAI, Google) for something real — not just a browser chatbot.
\- You learn fast and can translate a vague business problem into a clear technical spec.
\- Solid security hygiene — you know what a secrets manager is and you don't paste API keys into Slack.
\- You want the executive seat, not just the paycheck.
\*\*Nice to have:\*\* CS, MIS, or Computer Engineering background; experience with n8n/Make/Zapier/Pipedream; agency or marketing\-tech exposure (GHL, HubSpot, Meta Ads API, GA4\); familiarity with Claude Code or Cursor.
\*\*This probably isn't for you if\*\* you want a relaxed 9–5, you call yourself an "AI consultant" but haven't shipped anything in the last year, or you need constant direction.
\#\#\# Compensation \& benefits
\- \*\*Base salary:\*\* $75,000/year
\- \*\*Performance bonus:\*\* tied to documented savings, paid quarterly
\- \*\*Year\-two target:\*\* \~$150K total comp on hitting milestones
\- \*\*Benefits:\*\* health plan, paid time off, and a $3–5K/year hardware and tooling budget
\#\#\# How to apply
Apply with your resume and a link to something you've built — GitHub, a live demo, or a short writeup. Show us a project rather than a cover letter.
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\*Suggested Indeed fields:\*
\- \*Job type: Full\-time\*
\- \*Pay: $75,000/year\*
\- \*Schedule: Monday to Friday\*
\- \*Work location: In person (Lubbock, TX 79423\)\*
Pay: $47,034\.66 \- $56,643\.89 per year
Work Location: In person
Salary Context
This $47K-$56K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Fox Tone Media, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($51K) sits 71% below the category median. Disclosed range: $47K to $56K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Fox Tone Media AI Hiring
Fox Tone Media has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lubbock, TX, US. Compensation range: $56K - $56K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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