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About This Role
AI Optimization & Automation Specialist
Company: Everlife
Compensation: $135,000–$165,000 + Performance Bonus
Full-Time
Reports Directly To: Founders & CTO
About Everlife
Everlife is a fast-growing longevity-focused telehealth company operating at the intersection of healthcare, technology, AI, and consumer wellness.
Through EverlifeMD.com, we are scaling rapidly across the U.S. with strong in-house technology, automation, marketing, customer service, and creative infrastructure.
We have earned a 4.9 Trustpilot rating with 2,500+ reviews and are continuing to scale aggressively.
We are a fast-moving longevity company looking for people who want to grow with us, wear multiple hats, move quickly, and stay on the cutting edge of both health and technology.
The Role
Everlife is looking for an exceptionally strong AI Optimization & Automation Specialist to work directly with our Founders and CTO.
We already have substantial infrastructure in place.
Your role is to plug into that infrastructure, identify where AI can create leverage, design better workflows, connect tools and platforms, and continuously optimize how the company operates.
You do not need to be a traditional software engineer.
You do need to be highly technical, extremely comfortable with AI tools and APIs, and capable of working closely with our CTO and engineering team to turn ideas into production systems.
This role will work across:
Customer Service Operations Accounting CRM Klaviyo Creative Meta Analytics Internal Tools
Creative is a major part of the role, but this is ultimately a company-wide AI optimization position.
What You'll Do
You will identify opportunities across Everlife where AI, automation, and better system design can improve speed, efficiency, scalability, and decision-making.
Projects may include:
- Expanding AI workflows across Zendesk and customer service
- Optimizing internal operations
- Building AI-assisted workflows around QuickBooks and accounting
- Improving CRM, Klaviyo, email, and SMS automation
- Expanding our AI creative stack
- Working with Higgsfield, video, image, voice, and audio tools
- Helping improve Meta automation and creative feedback loops
- Designing AI agents and internal workflows
- Connecting SaaS platforms and internal systems
- Creating reporting and optimization systems
- Finding and eliminating repetitive manual work across departments
AI & Automation Expertise
You should have deep hands-on experience with:
- Claude / Anthropic
- OpenAI / ChatGPT
- Gemini
- Grok
- Higgsfield
- ElevenLabs
- AI image and video generation
- Voice and audio tools
- Multi-model AI workflows
- Prompt and context engineering
- AI agents
- Workflow automation
- Model routing
- Evaluations and QA
- AI cost and performance optimization
We want someone who knows how to choose the right model and tool for each problem—not someone who relies on one platform for everything.
Technical Fluency
You do not need to be a senior software engineer.
You do need to be technically fluent enough to work closely with our CTO and engineering team.
You should be comfortable with concepts such as:
- APIs
- Webhooks
- API documentation
- JSON
- Databases
- SaaS integrations
- Authentication
- Workflow logic
- Data flow between systems
- Automation platforms
- Basic scripting or technical prototyping
- Cloud-based tools
- Error handling and monitoring
You should be capable of understanding how systems connect, designing the workflow, testing integrations, and clearly communicating technical requirements to engineers when deeper development is needed.
Systems We Work With
Experience with any of the following is valuable:
- Zendesk
- Klaviyo
- QuickBooks
- CRMs
- Meta
- Higgsfield
- ElevenLabs
- Claude
- OpenAI
- Gemini
- Grok
- Supabase
- Cloudflare
You do not need to be an expert in every platform.
You need to be exceptionally good at learning new tools quickly and figuring out how to use them together.
Who We're Looking For
We're looking for a high-agency, hungry AI operator and optimizer.
You should constantly be asking:
**Can AI do this better?
Can this be automated?
Can these systems be connected?
Can we reduce human work?
Can we improve quality or speed?
Can we use a better model or tool?**
You should be comfortable moving between departments and solving very different problems.
One day you may be improving an AI creative workflow.
The next day you may be redesigning Zendesk automation.
Then working with the CTO on a QuickBooks or CRM integration.
You should move quickly from:
Problem → AI Solution → Workflow → Test → Optimize → Scale
We care much more about what you've built, how you think, and how quickly you learn than traditional credentials.
You're Probably a Great Fit If
- You're obsessed with AI and automation
- You test new AI tools constantly
- You have strong experience with Claude, ChatGPT, Gemini, and Grok
- You understand APIs and system integrations
- You're familiar with CRMs and SaaS tools
- You understand creative AI and tools like Higgsfield
- You're comfortable with Zendesk, Klaviyo, QuickBooks, or similar platforms
- You think in workflows and systems
- You are highly analytical and optimization-focused
- You can work effectively with technical engineers without needing to be one
- You move extremely fast
- You like wearing multiple hats
- You want to grow inside a rapidly scaling company
Not a Fit If
- Your AI experience is limited to writing prompts
- You only know one AI platform
- You aren't comfortable with APIs or technical workflows
- You need detailed instructions before getting started
- You don't experiment with new tools
- You prefer narrow roles and fixed responsibilities
- You don't think in terms of optimization
- You move slowly
Compensation
$135,000–$165,000 base + meaningful performance bonus.
For an exceptional candidate, we are flexible.
How to Apply
Don't just send a résumé.
Show us what you've built or optimized.
Send examples of:
- AI workflows
- Automations
- Agents
- Creative AI systems
- API-driven projects
- CRM or lifecycle automation
- Zendesk workflows
- Klaviyo systems
- QuickBooks or operations automation
- Multi-model AI workflows
- Demos
- Loom walkthroughs
- Personal projects
We would rather see a smart system you built or optimized than another certification.
If you're obsessed with AI, relentlessly focused on optimization, technically fluent, move fast, and want to work directly with Everlife's Founders and CTO improving AI across the entire company—we want to meet you.
Pay: $140,000.00 - $185,000.00 per year
Benefits:
- Flexible schedule
- Health insurance
- Paid time off
Work Location: Remote
Salary Context
This $140K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Everlife LLC, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($162K) sits 23% below the category median. Disclosed range: $140K to $185K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Everlife LLC AI Hiring
Everlife LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $185K - $185K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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