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What This Role Is
For a century, medicine has treated the body as a chemical system to be dosed. We treat it as what it actually is: a bioelectric system that can heal itself when its energy is coherent. Two decades of research, 300\+ peer\-reviewed sources, and a working product ecosystem stand behind that.
That ecosystem is already real and fully integrated. Wearables that read the body continuously. Devices that repair injury and restore the body's fields. And an AI that reads voice, pulse, blood, and tongue to understand someone's health more deeply than any annual physical, then guides them toward getting better.
Detect. Correct. Protect. One connected system.
The science works. The results speak for themselves. The only thing between that and millions of transformed lives is whether people ever find it, trust it, and start. That is your job.
You own how Energy4Life reaches the world. You will architect and operate the AI growth engine yourself, paired with a dedicated engineer who builds at your direction — and you will hire and lead a small team. We want someone who thinks at the level of the whole marketing function and still keeps their hands on the machine that drives it.
What You'll Own
Two audiences, and the engine that serves both.
The consumer — where the growth and the mission live
Picture the person on the other end: energy depleted, health stalled, having followed the
conventional path and found it wanting. They find us through a podcast, the documentary, a
social clip, a friend. They download the app. From that moment, every interaction should feel
shaped by who they are — not a demographic, not a persona. Them.
- Acquisition. Build automated funnels attached to every traffic source — podcast appearances, documentary viewership, webinars, organic and paid social — each with a destination engineered to convert.
- In\-app experience. Own onboarding, engagement, coaching content, product introductions, and retention inside THE FIELD app — where the relationship is built and most revenue is earned.
- Lifetime value. Move single\-product buyers into multi\-product integrators, using their own health data and progress as the signal for what to introduce next. LTV is the truest measure of whether this role is working.
The practitioner channel — a force multiplier
Clinicians, integrative providers, and energy\-medicine specialists are central to how our products reach the world. A practitioner who adopts our platform brings their entire client base with them — their clients become our clients.
- Build a qualified lead pipeline. Targeted content, nurture sequences, and behavioral scoring that surface practitioners ready to engage — and hand them to sales well\- prepared.
- Keep practitioner messaging and the consumer brand reinforcing each other, never competing.
The AI growth engine — the asset under all of it
This is what makes personal\-at\-scale possible, and it is yours to architect. Your dedicated AI engineer builds the systems quickly; you decide what they are, how they connect, and what good looks like. It begins on day one and compounds into one of the most valuable assets the company owns.
- Turn every signal — app behavior, content engagement, purchase history, email, in\-app health inputs — into living customer profiles that reflect real intent and product fit, not crude segments.
- Design the AI layer inside the app so the coaching, content, and recommendations feel genuinely personal: a knowledgeable guide that understands you, not a product talking at you.
- Build behavior\-triggered automation and predictive models — churn, upgrade\-readiness, content appetite — and act on them before the moment passes.
The documentary launch
Our documentary film is a category\-defining moment, and you own its launch end to end.
- Run the full launch: pre\-launch list building, affiliate and partner activation, social campaign, earned media, post\-launch conversion architecture.
- Design the journey from viewer to subscriber to app user to product customer — and turn it into a repeatable playbook for every content event after it.
The team
You hire and lead a small marketing team, plus your AI engineer. You set the bar, define what good looks like, and stay close enough to the work to coach by example rather than from a distance.
What You Need
Essential
- You build with AI. You can stand up funnels, sequences, automations, and intelligence systems using AI tools yourself — and direct an engineer to build the heavier infrastructure. You architect; you do not sit and wait. This is the single most important capability the role demands.
- A real track record driving consumer growth — acquisition, conversion, retention, LTV — with results you can point to and explain.
- Experience building or owning marketing automation, CRM, and behavioral analytics systems.
- Daily fluency with AI tools as instruments — to understand customers, generate and test content, automate, and surface intelligence — not as novelties.
- Sharp analytical instincts: you read a funnel, find the leak, and design the fix.
- Experience turning content traffic (podcasts, video, events) into customers, and experience with in\-app growth — onboarding, engagement, subscription retention.
- The judgment to lead a small team and the hunger to still build with your own hands.
- Genuine curiosity about health, bioenergetics, longevity, or integrative medicine — enough to speak about this work with credibility and care.
Advantageous
- Marketing health, wellness, wearable, or medical\-device products to consumers.
- Building AI\-powered personalization or behavioral\-intelligence systems hands\-on.
- Documentary, film, or large\-scale content\-event launches.
- Marketing to practitioners or through a professional / B2B2C channel.
How We'll Evaluate You
We don't care about your degree. We care about what you've built and what it did. Come prepared to show:
- A consumer funnel or lifecycle program you designed — what you built, what it drove, what you learned.
- Specific ways you've used AI to understand customers, personalize experiences, or automate growth — in practice, not theory.
- How you've turned content traffic into customers, and grown revenue from customers you already had.
- A system or team you led that made customers feel understood rather than marketed to.
- What about physics\-first medicine or bioenergetics pulls you in — and how fast you go deep on something new.
Compensation and Growth
- Competitive base salary.
- Performance bonuses tied directly to consumer revenue metrics: app acquisition, conversion, LTV, engagement, retention.
- Equity eligibility from Year 2 for exceptional contributors.
- Direct, ongoing collaboration with the Founder on strategy, content, and launch planning.
- The chance to build and lead the marketing function for a company redefining what healthcare can be.
The Standard
The future of healthcare is personal — not personalized in the marketing sense, but genuinely personal: built on the individual's biology, shaped by their own data, and delivered by an AI that actually understands them. You will build, and lead the team that builds, the growth engine that brings that to millions of people.
If that's the work you want to do — apply.
Benefits:
- Health insurance
- Paid time off
Work Location: In person
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 Energy4Life, 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. Director-level AI roles across all categories have a median of $247,800.
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.
Energy4Life AI Hiring
Energy4Life has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US.
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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