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About This Role
AI Engineer — Build the AI Brain of a Clinical System. On a Real Platform. With Real Users.
- Location: Salt Lake City, Utah (preferred)
- Reports to: Founder
- Works closely with: Product/technical leadership and the practitioner channel
- Role Type: All\-in, high\-ownership builder role with real creative authority
- Compensation: Competitive base \+ performance bonuses. Equity eligibility in Year 2 for exceptional performers.
- Performance Standard: Exceptional or not retained
READ THIS FIRST
This is a rare combination, and we know it.
We need someone who can do two things most engineers treat as opposites. You'll build a brand\-new agentic AI clinical co\-pilot from a near\-blank page — the kind of greenfield AI work people dream about. And you'll do it on top of a real, live health platform with years of .NET behind it and practitioners depending on it every day.
That second part is not a footnote. The future doesn't get built on a clean slate here — it gets built on a working system that already serves people, which you'll modernise as you go. If "legacy archaeology" makes you groan, this isn't your role. If it makes you think "give me the codebase, I'll find the business logic and bring it forward" — keep reading.
You'll have direct founder access and the freedom to make real product and architecture decisions. We don't want a pair of hands. We want someone with the taste and judgment to make the Co\-Pilot genuinely excellent — and the engineering depth to ship it fast.
If you're looking for:
\- A pure greenfield job with no old code to touch
\- A clear playbook someone else wrote
\- A comfortable pace where you learn the domain before you deliver
\- A title without real authority
This role is not for you.
WHY THIS MATTERS
For 100 years, medicine has treated the body as a chemical system. Drug discovery costs double every nine years while efficacy halves. Chronic disease keeps accelerating despite trillions in spending. The model is failing — spending more to produce less — and everyone inside it knows it.
We treat the body as what it actually is: a bioelectric system. Every cell holds a voltage, communicates through electromagnetic signals, and stores energy in structured water. When those fields are coherent, biology self\-organises and the body heals. We have two decades of R\&D, 300\+ peer\-reviewed sources, proprietary diagnostic and therapeutic technology, and a global practitioner network already using it.
What's missing is the intelligence layer — the AI that turns all of that into the right next move for a real patient. That's what this role builds.
WHAT YOU'LL BUILD — THE FIELD CLINIC CO\-PILOT
Field is our health system: one intelligence reading the whole body — pulse, face, voice, tongue, blood labs, wearable data, and full history — and turning it into the right next action. There are two sides: the consumer app, and the practitioner system you'll own the engine for.
The Field Clinic Co\-Pilot is the AI that practitioners work alongside. It reads every signal a patient generates, drafts the next move — labs, protocols, refills, follow\-ups — routes it to the practitioner for approval, delivers what they sign off on, and then measures whether it worked. The loop is the product:
Measure. Decide. Deliver. Measure again.
A few things that define how it has to work, and that you'll be responsible for getting right:
\- The human stays in the loop, always. The AI never orders a lab or sends a prescription on its own. It drafts; a licensed practitioner reviews, edits, or declines; only then does anything reach a patient. Most approvals should take under 60 seconds — that "fast and safe" balance is an engineering and design problem you'll own.
\- One engine, two faces. The consumer AI coach and the clinical co\-pilot are the same brain on a shared backend — one doing intake, the other drafting clinical protocols. Build the approval and delivery workflow once; it serves both.
\- It's branded around the clinic. When a patient comes in through a practitioner, the experience carries that clinic's name, photo, and preferred therapies. The intelligence underneath stays consistent; the front the patient sees belongs to their clinician.
\- It closes the gap between visits. Patients fail in the silence between appointments. The Co\-Pilot's job is to make sure something is watching the whole picture continuously, and surfacing what matters to the practitioner before it becomes a problem.
You'll build this agentically: multi\-modal inputs in, drafted clinical actions out, a tight approval workflow, real delivery rails, and a measurement loop that makes the next reading sharper than the last.
WHAT YOU'LL BE RESPONSIBLE FOR
1\. Build the Co\-Pilot — agentic, fast, excellent
Design and ship the agentic AI that ingests multi\-modal patient data and drafts the next clinical move for practitioner approval. You'll make real calls on how the agents are structured, how context and memory are managed, how safety and confidence are surfaced, and what the practitioner's approval experience feels like. Prototype aggressively, test in reality, kill what doesn't work, scale what does.
2\. Modernize the platform underneath it
The existing practitioner platform runs on legacy .NET. You'll keep it healthy while pulling it forward — refactoring and migrating components to current frameworks, standing up new services on a modern stack, and doing it incrementally without breaking what practitioners rely on today.
3\. Own the full stack
Database through API to front end. You'll design schemas, write and optimise queries, build and integrate APIs, wire up third\-party services, and deliver working interfaces. You don't have to be the world's best at every layer — but you need to move confidently across all of them and know when something's off.
4\. Work AI\-first
We expect AI to do as much of the building as possible — code generation, testing, research, automation. You already use AI to accelerate your own work; here it's a core operating principle, not a perk.
THE STACK
Existing / legacy stack — you must be comfortable working in this:
- .NET Framework 4\.5
- C\# with ASP.NET MVC 3 and above
- Entity Framework and LINQ
- HTML / CSS
- JavaScript and jQuery
- Microsoft SQL Server (MSSQL)
- TFS for source control
- IIS 8
Modern stack — where the platform is heading:
- .NET Core / .NET 8\+
- REST API design, build, and integration with third\-party services
- Microsoft Azure — App Services, SQL Database, Functions, Key Vault, Storage
- A modern JavaScript framework — React, Angular, or Vue (tell us your preference and why)
- Git, alongside or replacing TFS
Authentication:
- Duende IdentityServer (OAuth2 / OpenID Connect) — the platform's self\-hosted auth service
Core competencies — must have:
- SQL Server: schema design, stored procedures, query optimisation, migrations
- API connectivity and integration
- Strong C\# fundamentals across both old and new framework versions
Strongly desirable:
- Experience migrating legacy .NET Framework apps to .NET Core / .NET 8\+
- CI/CD pipelines (Azure DevOps or GitHub Actions)
- Automated testing (unit and integration)
- Legacy archaeology — working with undocumented code, reverse\-engineering business logic, and refactoring incrementally without taking production down
- Real experience building with AI agents, LLM orchestration, and multi\-modal or time\-series data
WHO THIS ROLE IS FOR
- You're a strong C\# / .NET engineer who isn't afraid of old code — you can read an undocumented system, find the business logic, and bring it forward
- You've built real things with AI agents, not just prompted them — and you want to build a lot more
- You move across the full stack with confidence, from SQL schema to API to front end
- You have taste — you know what "excellent" feels like and you'll push for it
- You want founder access, real creative authority, and outsized ownership
- You ship fast, accept imperfect information, and prove ideas in reality instead of in decks
- You're drawn to the idea that medicine itself is about to change, and you want to help build what comes next
WHO THIS ROLE IS NOT FOR
- You only want greenfield work and won't touch legacy code
- You can prompt but you can't engineer
- You need someone to tell you exactly what to build
- You optimise for job longevity over impact
- You need a large org, long timelines, and established process to feel safe
HOW WE WORK
- Build first. Test in reality. Refine what survives. Scale what proves itself.
- AI does the work. Humans direct, decide, and quality\-check.
- Speed beats perfection. Depth beats speed when speed is fake.
- Prototypes, not slideware. Evidence, not opinions.
- Direct founder access. High autonomy. Very high expectations.
- Small team, big ambition, no bureaucracy.
REWARDS \& UPSIDE
- Competitive base salary \+ performance bonuses tied to real output
- Equity eligibility in Year 2 for exceptional performers — tied to contribution, not tenure
- Direct partnership with the founder and real authority over how the Co\-Pilot gets built
- Ownership of the AI engine at the centre of a clinical system practitioners use worldwide
- Early exposure to category\-defining IP spanning two decades of biophysics research and proprietary technology
- The chance to build something that will matter for a long time
WHAT YOU NEED TO SHOW US
We care about what you've actually built — not your degree or your resume polish.
- AI agents, LLM systems, or automations you've built — and what they did
- .NET / C\# systems you've shipped, especially anything where you modernised or rescued legacy code
- Full\-stack work where you owned database through front end
- Evidence you move fast with imperfect information and produce results
- Side projects and obsessive explorations — welcome, if the output is exceptional
INTERVIEW PROCESS
We'll give you real problem statements from our platform and the Co\-Pilot. Come ready to talk about:
\- How you'd architect an agentic clinical co\-pilot with a human\-approval loop that's both fast and safe
\- How you'd approach modernising a live .NET Framework platform without breaking production
\- How you'd design the data and API layer connecting multi\-modal inputs to drafted clinical actions
\- What you've already built with AI agents — and what you learned
\- What about physics\-first medicine interests you, and how fast you'd go deep on the science
We're evaluating how you think, how fast you move, and whether you're someone our team would want to build with.
FINAL FILTER
If reading this makes you feel energised by the build, excited by the freedom and the founder partnership, and certain you're the rare engineer who can do both the legacy work and the AI future — apply.
If it makes you feel like you'd need to be convinced — this probably isn't your role, and that's okay.
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 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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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