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About This Role
About Eleanor Health At Eleanor Health we are helping people affected by addiction live amazing lives. We work to fulfill this goal by building and growing the first addiction and mental health services provider designed to deliver a longitudinal, whole-person, value-based model of care for each community member. Our team is ambitious and committed to this unique opportunity, and understands the importance of attracting strong and experienced talent to make our vision for Eleanor Health a reality every day, and for every recovery journey we are entrusted to be a part of.
About the Role When someone searches for a suboxone doctor near them, asks an AI assistant where to get help with drinking, or reads a community thread about virtual addiction treatment, we want Eleanor Health to show up — accurately, warmly, and in a way that helps them take the next step.
Today, these responsibilities are distributed across several people without dedicated operational ownership. You would become the hands-on owner responsible for turning that strategy into consistent execution.
As the Reputation & AI Visibility Specialist, you will own how Eleanor Health appears across the places people actually look for treatment: business listings and healthcare directories in every market we serve, reviews, recovery communities, earned media, and the AI assistants and AI-generated search results that an increasing share of people now ask first.
This is a hands-on role at the intersection of local search, reputation management, community engagement, and the emerging discipline of getting recommended by AI. It is not a brand awareness position with soft metrics — it is an acquisition role with a number attached, and it carries a capability our growth plan depends on.
We will also be honest with you about what you would be walking into. Today we manage our directory presence off the side of several desks, and we do it poorly. Reviews are unmanaged. Nobody is watching what community members say about us. You would inherit a real mess and a real opportunity, in roughly equal measure.
This role reports to the Sr. Manager, Organic & AI Visibility.
What You'll Do
Local Presence & Directory Integrity* Own Eleanor Health's business listings across every market we serve, including Google Business Profile, Apple Business Connect, and Bing Places
- Manage healthcare directory and health plan provider directory listings, and the data aggregators that feed them
- Audit and remediate name, address, and phone consistency across all platforms and markets
- Establish and maintain category strategy, service listings, hours, attributes, and profile completeness
- Build and maintain a system of record for listings so accuracy does not depend on any one person's memory
- Identify and resolve listing suppressions, duplicates, and unverified profiles
- Partner with content and web owners to ensure location data, service information, and other entity information remain consistent between EleanorHealth.com and external listings/directories
Reviews & Reputation Management
- Design and operate an ethical review generation program in partnership with clinical and access teams, with patient privacy protected absolutely and no incentivization or gating
- Respond to reviews across platforms with warmth, care, and without defensiveness
- Monitor review volume, rating, and sentiment by market and surface patterns to clinical and operations leadership
- Partner with Compliance and Legal to ensure all review practices meet HIPAA and 42 CFR Part 2 requirements
- Maintain an escalation process for reviews or reputation issues requiring clinical, compliance, legal, or operational response
AI Visibility & Discoverability
- Establish a baseline for how often and how accurately AI assistants and AI-generated search results recommend Eleanor Health, then improve it
- Monitor for material misinformation about Eleanor Health, and about addiction treatment generally, in AI-generated responses
- Identify opportunities to strengthen Eleanor Health's entity signals, citations, authoritative third-party mentions, and other external signals that influence AI discoverability and recommendation
- Partner with the content, web, and technical SEO functions to translate AI visibility findings into prioritized website improvements, including structured content, metadata, internal linking, location content, structured data, and other discoverability enhancements
- Track and report AI citation and recommendation share on a regular cadence
- Stay current in a discipline that is being invented in real time, and help invent it here
- Implement approved discoverability updates in Sanity and other marketing platforms where appropriate
Community Engagement
- Monitor how Eleanor Health and virtual addiction treatment are discussed in recovery communities, forums, and social platforms
- Participate transparently and helpfully as an identified representative of Eleanor Health, never anonymously
- Surface community sentiment, recurring concerns, and misinformation to marketing and clinical leadership
- Support and manage organic social activity where it directly advances discoverability, community engagement, or reputation goals
Earned Media & Editorial Distribution
- Manage the day-to-day public relations agency relationship in alignment with established priorities, including setting priorities, triaging inbound opportunities, and holding the agency accountable to outcomes
- Brief Eleanor Health spokespeople ahead of media opportunities
- Own hands-on editorial distribution and syndication efforts to strengthen Eleanor Health’s authority, earn relevant backlinks and citations, and grow referring domains in priority markets
- Evaluate earned media and distribution opportunities based on their potential contribution to authority, discoverability, reputation, and enrollment impact
Measurement & Reporting
- Build and maintain your own reporting on listings accuracy, review performance, AI citation share, and earned coverage
- Partner with our analytics consultant to attribute non-paid new member volume accurately
- Report performance on a monthly cadence and be able to defend an attribution claim
You'll Be a Good Fit If You:* Are a genuine hands-on operator who does the work rather than directing it — multi-market listing management is tedious and unglamorous, and you have to find that acceptable
- Find the constraints of a regulated healthcare environment energizing rather than frustrating, and raise your hand before crossing a line rather than after
- Write well, and understand that tone matters enormously when the audience is someone on the hardest day of their life
- Are self-directed and able to identify and resolve gaps without being prompted
- Are comfortable being measured, and would rather own a number than a set of activities
- Approach your work with compassion, knowing that a correct directory listing is sometimes the difference between someone finding care and giving up
Skills and Competencies:* Experience — roughly 3+ years in digital marketing with real depth in local SEO, reputation management, or digital PR, including hands-on multi-location listing management
- Reputation management — experience building or running a review generation and response program
- Tooling — fluency with SEO and local tooling. Our stack includes Ahrefs, BrightLocal, Local Falcon, Otterly.ai, and Screaming Frog. You should be comfortable in tools like these and able to tell us which ones we are using wrong
- Writing — strong written communication across review responses, community posts, and media briefs
- Analytical — comfort building your own reporting and defending your numbers
- Judgment — sound decision-making in a space with hard compliance constraints around patient privacy
- Adaptability — comfortable learning new systems and working in a discipline that is still taking shape
Helpful but not required:
- Behavioral health, addiction treatment, or healthcare marketing experience
- Early hands-on work in generative engine optimization or AI search visibility
- Working knowledge of structured data and schema markup
- Real, disclosed participation in online communities
- Multi-market or multi-location experience at any scale
What This Role Is Not:
We would rather be clear now than waste your time.
- Not primarily a media relations seat. We retain a PR agency for pitching and placement; you would direct them. If hands-on media relations is the work you love most, you would find this role only partly satisfying.
- Not a paid media role. Paid search and paid media sit elsewhere on the team.
- Not a content writing role. We have a content strategist and a copywriter. You will write a great deal, but not long-form content.
- Not a growth-hacking role. No incentivized or gated reviews, no undisclosed community participation, and no shortcuts that trade a person's trust for a metric. We will turn down tactics that work if they are not honest, and we would want you to be the person raising your hand first.
Compensation & Benefits The total target compensation range for this position is $70,000-80,000. The actual compensation offered depends on a variety of factors, which may include, as applicable, the applicant’s qualifications for the position; years of relevant experience; specific and unique skills; level of education attained; certifications or other professional licenses held; other legitimate, non-discriminatory business factors specific to the position; and the geographic location in which the applicant lives and/or from which they will perform the job.
Eleanor Health offers a generous benefits package to full-time employees, which includes:
- Generous Paid Time Off (prorated for new employees) - unplug, relax, and recharge!
- + 12 Vacation days + 12 Wellness (Sick) days + 3 Floating holidays + 9 Company-observed holidays + Jury Duty, Voting Leave, and other forms of paid time off may be available
- Eleanor-paid medical and dental insurance plans, with affordable vision coverage - We are a health first company, and we strive to make our plans affordable and accessible.
- 401(k) plan with 3% match - We are excited to be able to support the long-term financial well-being of our team in a way that reinforces Eleanor’s commitment to equity.
- Short-term disability - We understand that things happen, we want you to feel comfortable to take time to get better.
- Long Term Disability - Picks up where Short Term Disability leaves off (employee paid).
- Life Insurance - Both Eleanor and employee-paid options are available.
- Family Medical Leave - Eleanor Health’s Paid Family & Medical Leave (“PFML”) is designed to provide flexibility and financial peace of mind for approved family and medical reasons such as the birth, adoption, or fostering of a child, and for serious health conditions that you or a family member/significant other might be facing.
- Wellness Perks & Benefits - Mental Health is important to us and we want our employees to have the accessibility you deserve to talk things through, zen with a mindfulness app, or seek assistance from health advocates
- + Mindfulness App Reimbursement + EAP with eight face-to-face sessions + Virtual health and wellness visits covered by our medical plan.
Salary Context
This $70K-$80K range is in the lower quartile 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 Eleanor 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($75K) sits 65% below the category median. Disclosed range: $70K to $80K.
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.
Eleanor Health AI Hiring
Eleanor Health has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $80K - $80K.
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.
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