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About This Role
ABOUT DR. BERG NUTRITIONALS
Dr. Berg Nutritionals is a leader in the health and wellness industry focused on addressing the root causes of health concerns. With nearly 15 million YouTube subscribers, 7,000\+ educational videos, and one of the most\-trafficked health education websites in the world, Dr. Berg has built a massive audience — but that authority is underrepresented in the AI search platforms that increasingly shape how people discover health information.
We offer a dynamic work environment with opportunities for growth, where you can contribute to helping millions achieve better health through education, premium supplements, and holistic practices like the Healthy Keto® diet.
ROLE SUMMARY
We're hiring a part\-time SEO Strategist to lead an authority\-first AI search strategy for DrBerg.com and Shop.DrBerg.com.
The central challenge: Dr. Berg has massive authority in video and traditional search but is underrepresented — and sometimes actively displaced by competitors — in AI search answers on health topics he should own. This role will architect the strategy to make Dr. Berg the most defensible, retrievable, and commercially relevant voice on high\-value health questions across ChatGPT, Perplexity, Gemini, and Google AI Overviews.
You'll design topic authority clusters, build an entity trust framework, create a citation magnet strategy, coordinate with an AI content pipeline and medical review system, and guide how millions of people find Dr. Berg's content and products — with traditional SEO as the foundation, not the ceiling.
This is not a maintenance role. You'll be building something new — a 12\-month phased program that sequences topic authority, entity trust repair, and buyer\-intent capture in the right order.
WHY JOIN DR. BERG NUTRITIONALS
- Lead AI search strategy for one of the most\-trafficked health education websites in the world
- Own a high\-visibility, greenfield program with direct executive and founder support
- Shape how AI platforms understand, cite, and recommend a major health brand
- Collaborative, mission\-driven team focused on helping people live healthier lives
- Competitive compensation and opportunities for expanded scope as the program grows
WHAT YOU'LL DO
AI Authority \& Entity Trust Strategy
*This is the primary focus of the role.*
- Audit Dr. Berg's current presence across AI platforms (ChatGPT, Perplexity, Gemini, Google AI Overviews) — identify where he's cited, where he's ignored, where competitors fill the gap, and how his entity frame is characterized
- Design and prioritize topic authority clusters (“battleground clusters”) based on existing Berg authority, search and query demand, commercial value, practical translation gaps, and citation defensibility
- Architect the entity trust layer — credential framing, methodology pages, medical review visibility, and strategic “where Berg agrees and differs from mainstream guidance” positioning
- Develop citation magnet concepts — symptom maps, root\-cause frameworks, diagnostic tools, calculators, and evidence tables designed to be cited by both humans and AI retrieval systems
- Build the strategic sequencing: topic authority first, entity trust in parallel, buyer\-intent capture last — and hold the team to that order
- Define the publishing architecture: what lives on the main domain vs. a reference subdomain vs. third\-party surfaces, and why
AI Content Pipeline \& Search Optimization
- Drive topic selection for the AI content pipeline by identifying high\-value clusters, AI citation gaps, and content opportunities — organized by authority clusters, not just keyword volume
- Classify every content asset by governance tier (consensus\-friendly, Berg extension, or contested) before it enters the pipeline
- Coordinate with the AI Automator Engineer to ensure all AI\-generated content is optimized for LLM retrievability — direct answers first, mechanism layers, structured FAQ blocks, proper citation formatting, and schema markup
- Coordinate with the medical review system to ensure credentialed co\-authorship signals (MD/PhD bylines, reviewer markup, lastReviewed dates) are visible to both users and AI retrieval systems
- Design content architecture for each cluster: canonical authority pages, FAQ cluster pages, mechanism explainers, symptom/pattern pages, and action/decision pages
- Track per\-article organic traffic and AI citation performance; feed data back into topic selection and cluster prioritization
Technical SEO \& Site Health
- Conduct monthly technical SEO audits and maintain site health at or above target thresholds
- Monitor and resolve crawl errors, broken links (404s), redirect issues, mixed content, and indexability problems
- Own robots.txt, XML sitemap, and canonical tag configuration
- Track and improve Core Web Vitals (LCP, CLS, INP), page speed, and mobile usability
- Own schema markup strategy — including MedicalWebPage type, author/reviewer structured data, and lastReviewed fields
- Log, triage, and track technical fixes in project management tools; escalate development issues and ensure resolution before the next audit cycle
Measurement, AI Citation Tracking \& Reporting
- Validate Google Analytics (GA4\), Google Tag Manager, event tracking, and conversion tracking
- Build and maintain an AI citation measurement system tracking: share of mention, recommendation rate, citation quality, entity framing, buyer\-intent appearance, competitor substitution, and topic\-cluster ownership
- Run target prompt audits: top 25 prompts monthly, full 100 quarterly, across all major AI platforms
- Judge every asset by movement — did mentions rise, did recommendations rise, did citations improve, did critics define the frame less — not just “did we publish”
- Deliver reports on a consistent cadence:
Weekly — Traffic \& Performance Report (organic traffic, engagement, bounce rates, conversions)
Weekly — Keyword Ranking \& Visibility Report
Monthly — Technical SEO \& Site Health Report
Monthly — AI Citation Audit (share of mention, entity framing, competitive displacement)
Monthly — Backlink Report (active backlinks, acquired/lost, domain authority)
Backlink Building \& Authority
- Assess the current backlink profile, identify broken or low\-quality links, and research high\-authority link targets aligned with the cluster strategy
- Execute outreach campaigns (digital PR, earned media, strategic partnerships) to acquire high\-quality backlinks from authoritative health and wellness publications
- Support selective third\-party trust transfer — podcast appearances with transcript pages, guest explainers on aligned sites, and syndication of citation magnets
- Coordinate with the medical reviewer bench and credentialed co\-authorship system to build organic authority signals
- Document, track, and report on backlink placements and domain authority gains
WHAT YOU'LL BRING
Required
- 5\+ years of hands\-on SEO experience with demonstrated strategic thinking — not just tactical execution. You should be able to design a 12\-month phased authority program, not just run monthly audits
- Demonstrated experience owning technical SEO on a large, content\-heavy website (thousands of URLs) in a YMYL vertical
- Demonstrated understanding of how large language models retrieve, weight, summarize, and cite health content — not just awareness that AI search exists, but hands\-on experience or a clear framework for engineering content to improve AI citation outcomes
- Experience designing topic authority cluster architectures — canonical pages, FAQ clusters, mechanism explainers, symptom pages, and decision pages organized around core topics, not just flat keyword lists
- Understanding of E\-E\-A\-T as it applies to YMYL content in AI retrieval — specifically how credentialed authorship, medical review signals, and schema markup influence citation probability
- Experience with or strong conceptual understanding of entity optimization — how AI systems build entity profiles, what signals strengthen them, and how to repair a “known but not default\-authoritative” trust frame
- Proficiency with the core SEO tool stack: Google Search Console, GA4, Ahrefs and/or SEMrush, Screaming Frog, PageSpeed Insights, Surfer SEO or Clearscope
- Strong working knowledge of Core Web Vitals, schema markup (including medical content types), redirects, canonicalization, and mobile optimization
- Experience tracking and managing SEO issues through project management tools (ClickUp, Trello, or similar)
- Ability to translate complex strategy into clear, actionable recommendations for both technical and non\-technical stakeholders
- Strong written communication and reporting skills
Preferred
- Experience in health, wellness, nutrition, or supplement industry — particularly with FDA/FTC content compliance considerations
- Familiarity with WordPress and publishing workflows
- Experience with structured data markup for medical content — MedicalWebPage schema, author/reviewer markup, lastReviewed fields
- Experience working with credentialed co\-authorship or medical review systems
- Experience with AI citation tracking tools (Searchable.ai, Profound, or similar)
- Experience with content optimization platforms (Surfer SEO, Clearscope)
- Experience partnering with AI engineers, content teams, and compliance teams in a cross\-functional workflow
Tools You'll Use
Google Search Console · GA4 · Google Trends · Ahrefs · SEMrush · Screaming Frog · Surfer SEO · Rank Math · PageSpeed Insights · AI citation tracking platforms · Trello · ClickUp · WordPress
Work\-from\-Home Requirements
- Up\-to\-date Mac or Windows computer with anti\-virus protection
- Reliable high\-speed internet connection
- Quiet, distraction\-free workspace
- Tech\-savvy and comfortable learning new tools
- Comfortable using Microsoft Office tools (Excel, Outlook, Teams, Word, OneDrive)
POSITION DETAILS
Pay Range: $65–$85/hour depending on experience
Hours: 15–20 hours per week
Location: Fully Remote within the US
HOW TO APPLY
Submit your resume along with:
- Pick one health topic (e.g., insulin resistance, intermittent fasting, electrolyte supplementation). Search for it in ChatGPT and Google AI Overviews. In 1–2 paragraphs, describe what you observe about which sources get cited, why, and what a health brand would need to do to become the default\-cited authority on that topic.
- Examples of organic growth or AI citation results you've driven
- Your availability and hourly rate expectations
*Dr. Berg Nutritionals is an equal\-opportunity employer. We welcome applicants from all backgrounds.* *We are not currently hiring international contractors.*
Salary Context
This $135K-$176K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Dr. Berg Nutritionals, 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. This role's midpoint ($156K) sits 14% below the category median. Disclosed range: $135K to $176K.
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.
Dr. Berg Nutritionals AI Hiring
Dr. Berg Nutritionals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $176K - $176K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 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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