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About This Role
Everlywell is a digital health company pioneering the next generation of biomarker intelligence—combining AI-powered technology with human insight to deliver personalized, actionable health answers. We transform complex biomarker data into life-changing insights—seamlessly integrating advanced diagnostics, virtual care, and patient engagement to reshape how and where health happens.
Over the past decade, Everlywell has delivered close to 1 billion personalized health insights, transforming care for 60 million people and powering hundreds of enterprise partners. In 2025, an estimated 1 in 94 U.S. adults received an Everlywell test, solidifying our spot as the #1 at-home testing company in the country. Fueled by AI and built for scale, we’re breaking down barriers, closing care gaps, and unlocking a more connected healthcare experience that is smarter, faster, and more personalized.
We are looking for a Director, Consumer Growth (AI-Native) to own paid and organic acquisition for the Consumer business and build the agent infrastructure that runs it. This is a hands-on role: you will set strategy across paid social, paid search, SEO, and organic, then build and operate the AI agents that execute media buying, creative iteration, and reporting with limited day-to-day human direction. You are the person who decides what the agents do, builds the systems that let them do it, and steps in when they can't.
You will turn growth opportunities into sharp commercial hypotheses, test them fast, and decide what deserves investment and scale, grounded in customer needs and unit economics. Secondarily, you will extend this same approach into new revenue vectors for the business: fast prototypes, landing pages, and fake-door tests that validate commercialization ideas before we commit real investment.
What you'll do:
Build and operate the AI agent infrastructure for growth
- Design and build the agent orchestration layer that runs paid media with limited human direction, using tools such as Grokbot, Ryze, Claude, and platform-native AI (Advantage+, Performance Max, automated bidding).
- Build an AI-assisted creative engine that generates, tests, and iterates ad concepts at high volume, within healthcare advertising policies and compliance standards.
- Define what agents own outright, what needs human review, and how failures get caught, and stay close enough to the work to prototype and troubleshoot it yourself.
Own and scale paid and organic acquisition
- Own paid acquisition across paid social (Meta, TikTok, and emerging platforms) and paid search (Google, Bing): strategy, budget, bidding, audiences, and day-to-day optimization.
- Own organic acquisition, including AEO/GEO, SEO, and content, leveraging the AI architecture above.
- Manage a media budget of $5M+ annually against CAC, ROAS, and payback targets, reallocating spend weekly based on performance.
- Lead measurement in a privacy-constrained environment: incrementality testing, attribution, MMM-informed planning, and HIPAA-aware tracking.
- Manage agency and freelance partners, holding them to clear performance standards.
Prototype new revenue vectors
- As a secondary focus, identify new revenue vectors for the business and validate them fast with landing pages, fake-door tests, and quick prototypes before committing real investment.
- Run a repeatable system for moving from insight to prototype to experiment to scale decision on new commercialization ideas.
Build and manage the team
- Lead and develop a small, high-output pod, managing a blended team of AI agents and contractors and matching each task to the right mix of the two, partnering closely across product, engineering, marketing, analytics, and compliance.
What success looks like:
In the first 90 days
- Audit paid, organic, and tracking; deliver early efficiency gains.
- Stand up the first agent-run workflows for campaigns or creative, with clear human checkpoints.
- Establish the pod's operating model and a prioritized experiment backlog.
In the first 12 to 18 months
- Improve blended CAC and payback while scaling spend across paid and organic.
- Manage AI agent-powered campaigns and creative workflow that operates at scale with minimal headcount.
- Demonstrate commercial proof through at least one validated new revenue vector, with a credible path to scale.
What you bring:
- 10+ years in growth or performance marketing, including 5+ years managing paid social and paid search directly in-platform.
- A track record managing multi-million-dollar media budgets against CAC and payback targets.
- Hands-on experience building or orchestrating AI agents or automation for campaign management, creative production, or performance analysis, with examples you can walk us through.
- Experience owning organic channels (SEO, content, or organic social) alongside paid.
- Strong command of growth economics and measurement, including incrementality testing and post-iOS attribution.
- Experience leading small, capable teams and managing agencies.
- BA/BS or equivalent experience.
Nice to have:
- Experience advertising in healthcare, telehealth, pharma, or another regulated category.
- Experience with DTC subscription or repeat-purchase businesses.
- Experience launching 0-to-1 products, landing pages, or fake-door tests to validate new revenue ideas.
You may be especially well suited in this role if you are:
- A growth leader who has owned a media budget end to end and wants to run it through AI agents instead of a traditional team.
- Someone who has built or deployed AI agents for real operational work, not just experimented with them.
- Equally comfortable inside an ad account, a prompt or agent config, and an executive strategy discussion.
Security Notice: Everlywell never requests fees, payment, or banking information at any stage of the recruitment process. Official communications and interview invitations will only come from verified email addresses ending in @everlywell.com or @everlyhealth.com. To ensure your application is secure, always apply directly through our official careers page at https://www.everlywell.com/careers/. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 Everly Health, 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. Director-level AI roles across all categories have a median of $272,250.
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.
Everly Health AI Hiring
Everly Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, 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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