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About This Role
Job Overview
Chief Content Officer
FutureStack AI Inc.
Full\-time · Remote (US, South Florida preferred)
We are seeking a dynamic and visionary Chief Content Officer (Hands\-On Leader) to spearhead our content strategy and execution across multiple platforms. This role is vital in shaping our brand voice, engaging audiences, and driving business growth through innovative content initiatives. As a hands\-on leader, you will blend strategic oversight with active involvement in content creation, ensuring high\-quality output that aligns with our organizational goals.
FutureStack AI is building AI agents and a cutting\-edge CRM for real estate professionals. We need a Chief Content Officer who owns the strategy AND the content across all publication channels, social media platforms, and points of contact with our audience
You'll partner directly with the founder. Besides publishing content to organically draw people in, and to create engagement, we would actually like to start monetizing training and education as quickly as possible \- so experience with this is a major plus.
The Role
You'll be the founder's primary partner on everything that relates to content, brand, online presence and engagement generation. Day\-to\-day looks like:
- Owning the editorial calendar and voice across LinkedIn (primary), X, Reddit, and Instagram
- Writing LinkedIn posts daily — company page and ghostwriting for the founder
- Shipping long\-form blog posts, weekly newsletters, landing\-page copy, sales collateral, and lead magnets
- Running GTM campaigns end\-to\-end — brief, hypothesis, channel mix, creative, measurement, retro
- Repurposing one piece of pillar content into 10\+ formats across channels
- Building cross\-channel publishing automation (Buffer, Make, n8n, Zapier, HubSpot)
- Designing AI\-assisted content workflows that let one person produce what used to take a team
- Reporting weekly on what shipped, what worked, and what's next
What We're Looking For (Required )
- 3\+ years as a marketer, content strategist, or creator with results you can point to
- A live body of work we can read today — blog, newsletter, LinkedIn, anything public
- Daily working fluency with AI tools (Claude, GPT, or equivalent) in your own workflow — not experimenting, using
- Bias toward shipping. We'd rather see a flawed post live than a perfect one in draft.
- The discipline to switch registers between technical "builder" depth and results\-driven "realtor" copy
- Comfort with direct feedback and quick iteration
Great to Have (None Are Mandatory)
- Degree in Digital Marketing, Communications, Journalism, or related
- 3–5\+ years in content marketing, growth, or creator roles, ideally including B2B SaaS or AI
- Deep LinkedIn fluency — algorithm, formats, company\-page growth, founder ghostwriting. Bonus if you can show before/after numbers.
- Multi\-channel range — X, Reddit, Instagram, YouTube. You know where each audience actually lives.
- GTM campaign ownership — you've run launches end\-to\-end, not just contributed
- Lead magnet design — checklists, templates, mini\-tools, gated reports. You've shipped a gated asset that drove signups.
- Newsletter chops — Substack, beehiiv, ConvertKit, Mailchimp, or similar
- Marketing automation — Buffer, Hootsuite, Make, n8n, Zapier, HubSpot
- Paid acquisition — LinkedIn Ads, Meta, Google. You know what a good ad looks like.
- SEO fundamentals — keyword research, on\-page, content clusters, technical basics
- Bilingual English/Spanish — significant plus for the real estate product
- Real estate, prop\-tech, local\-services, or B2B SaaS marketing background
- Sales\-driving content — case studies, comparison pages, decks, cold\-email sequences
- Video and short\-form — Reels, Shorts, basic editing in CapCut or similar
- Community building — Discord, Slack, Skool, Reddit at scale
- Design literacy — Figma, Canva, basic motion or video editing
- Founder\-led brand experience — you've helped a technical founder shape their public voice
What We Offer
- Meaningful early\-stage equity — this is a C\-suite role at a pre\-revenue company, and the equity reflects that
- Revenue share on campaigns you drive
- Salary after funding — which you'll have a direct hand in making happen
- Direct ownership of brand, content, and GTM across two live products
- Full AI tooling budget — writing tools, design tools, automation stack
- Remote\-first, with optional in\-person time in South Florida
- A founder who ships daily and respects operators who do the same
Please submit along with your resume:
- Your portfolio
- LinkedIn profile
- A short note on a content campaign you ran end\-to\-end: the goal, what you shipped, and the results.
- A short note on one of the highest\-performing pieces you've ever made and why it worked
Pay: $50,000\.00 \- $250,000\.00 per year
Work Location: Remote
Salary Context
This $50K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At FutureStack AI 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 $166,983 based on 13,781 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $188,800. This role's midpoint ($150K) sits 10% below the category median. Disclosed range: $50K to $250K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
FutureStack AI Inc AI Hiring
FutureStack AI Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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