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About This Role
Impact Clients is building a new organic growth channel around highly targeted social media pages in the supplement space, each with its own character, audience, voice, and content strategy.
The goal is simple: create genuinely useful content that earns attention organically, builds trust with specific demographics, and ultimately drives supplement sales through relevant affiliate offers.
As our AI Creative Strategist, you’ll own the thinking behind the content: developing new characters and avatars, finding angles that resonate with different audiences, writing hooks and scripts, analyzing what performs, and building a repeatable creative system that can scale across dozens of pages.
This isn’t only a content role.
What we learn from building this division will eventually become part of the curriculum taught inside our education company. You’ll help train our coaches and students on what’s working while also giving direct product feedback to our engineering team on the AI creative tools you use every day.
You’ll be helping build the playbook from zero.
ABOUT IMPACT CLIENTS
Impact Clients is a fast-growing tech-enabled education company helping everyday people build online businesses through a combination of education, coaching, community, and proprietary software.
Our culture is built around clarity, extreme ownership, high standards, and speed of execution.
We move quickly. We expect people to solve problems without constant direction. And we give talented people a significant amount of ownership when they demonstrate they can produce results.
If you thrive in environments where creativity and speed go hand-in-hand, where your work gets implemented immediately, and where you're expected to think like an owner rather than an employee, you'll fit in well here.
This is a fully remote, full-time role.
WHAT’S IN IT FOR YOU
- A blank canvas. Start from zero and shape the avatars, angles, accounts, and content strategy from the ground up.
- A path to leadership. Help build the team as the channel grows, with the opportunity to eventually lead the creative division.
- A team around you. Work alongside AI video editors and publishing support so your time stays focused on strategy, copy, and creative direction.
- Freedom to test. No ad spend approvals or endless creative bottlenecks. Put ideas into the market, learn what works, and iterate quickly.
- Simple production. Let the strength of the idea carry the content without the heavy production requirements often associated with paid creative.
- Exposure across three business units. Help build a new supplement division, shape how the playbook is taught inside our education business, and influence the roadmap of our proprietary software.
- Real influence beyond your own function. What you discover can become curriculum taught to thousands of students, while your daily use of AI tools helps determine what our engineering team builds next.
WHAT YOU’RE GETTING INTO - The Day To Day of this Role:
- Creating the AI avatars: determine which demographics deserve their own character, what those characters should represent, and which concepts should be killed.
- Generate 10–20 new ideas, hooks, and angles every day for short form 7-30 second reels.
- Write and direct scripts for 30–60 second organic Reels, then hand them to the AI video editing team for production.
- Own what gets tested across 5–10+ Instagram and Facebook accounts, each with its own personality, demographic, and content strategy.
- Match message to demographic precisely. A 45-year-old man and a 68-year-old woman should not receive the same hook, framing, or script.
- Turn genuinely useful information into content that earns trust first, allowing the offer in the bio to convert second.
- Study account-level performance and identify which avatars, hooks, formats, and topics are receiving organic distribution.
- Retire weak avatars and scale the characters and concepts that show traction.
- Turn winning creative patterns into repeatable systems rather than one-off successes.
- Help build the department once the model is proven, including future strategists, editors, and publishing support.
Approximately 10–20% of your time:* Once the model is proven, help build course content for the company's education division
- Run a weekly group coaching call helping students apply what we've learned.
- Give our software engineering team hands-on feedback about how to improve the internal AI tools you use every day.
WHO YOU ARE
- You come from the direct-response Meta advertising world, ideally within supplements, health, wellness, or another performance-driven ecommerce category.
- You have produced paid creative at meaningful volume — ideally hundreds or thousands of assets per month.
- You are a strong direct-response copywriter, and developing hooks, angles, and concepts is one of your favorite parts of the job.
- You've developed messaging for demographics significantly different from your own.
- You understand that what moves men aged 40–50 may be completely different from what moves women aged 60–70 — and you know how to adjust the creative accordingly.
- You can look at performance data and turn what is working into the next generation of creative ideas.
- You are comfortable testing aggressively, killing losing concepts quickly, and doubling down on winners.
- You are highly fluent with AI creative tools. Our current stack includes Higgsfield, Google Flow Omni, Openart, and CapCut.
- You can operate independently without needing a detailed SOP for every decision.
- Ideally, you've also worked on an info-product, course, or coaching-based offer, particularly in the make-money-online or start-an-online-business space (optional, though preferred).
The ideal candidate brings an unusual combination: ecommerce/supplement direct-response experience + strong creative strategy and copywriting + familiarity with info-product or coaching businesses.
WHAT SUCCESS LOOKS LIKE In Your First 90 Days On Our Team
30 Days:
Three to five avatars are defined, scripted, and actively in production. A consistent daily idea pipeline exists so the editing team is never waiting on creative direction. The first Reels are live across the initial accounts.
60 Days:
We have a clear understanding of which characters, hooks, topics, and angles Instagram and Facebook are actually distributing. Weak avatars are being retired, winning concepts are being expanded across additional accounts, and each audience is matched to a relevant offer.
90 Days:
A repeatable system exists from avatar brief creative concept script production published Reel performance analysis across 5–10+ accounts, without requiring you to manually touch every step.
You should also be able to clearly tell us what the department needs next, where the bottlenecks are, and who we need to hire to continue scaling it.
If this is role is exciting to you, then apply by filling out the questions below. Truly take your time with putting in quality effort into the Loom video, and our team will be in contact with you to schedule interview within 24-48 hours.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Impact Clients, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Impact Clients AI Hiring
Impact Clients has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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