Interested in this AI/ML Engineer role at Analyte Health?
Apply Now →About This Role
Analyte Health's mission is to provide easy, accessible and affordable online health care services for everyone. Everything we do focuses on helping our patients become healthier and happier. We have easy\-to\-use online platforms that provide fast, convenient, private and cost\-effective clinical services anytime, anywhere. Our trained health counselors will guide our patients every step of the way while our physicians are ready to deliver treatment. We provide innovative health care services through the elegant mixture of technology, science and patient\-centric care that gets our patients back on the path towards wellness.
*For more information, please visit our* *website www.analytehealth.com.*
The Role
Analyte Health is hiring a Senior Manager of Organic Search \& AI Visibility to drive traffic growth from Google Search and AI\-driven answer engines across consumer brands including HealthLabs, STDcheck, Stallion, Starfish, TreatMyUTI, and Paternity Labs.
This leader will be responsible for strategy, execution, and optimization of SEO, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO) across both traditional search and AI\-generated answers.
The role will also lead content strategy and production for onsite and blog content, using AI and tools like Profound to grow qualified traffic, improve AI visibility, and support revenue growth across multiple brands.
Key Responsibilities:
Own organic performance, visibility, and traffic
- Own traffic growth initiatives from organic search and answer engines across a portfolio of B2C brands; drive performance against goals for sessions, new patients, assisted conversions, and share\-of\-answer by brand and product line.
- Build quarterly and monthly organic growth plans; prioritize initiatives across technical SEO, on\-page optimization, GEO/AEO, and content based on impact and effort.
- Define and track key KPIs for SEO/GEO/AEO, including rankings, organic traffic, AI citations or mentions, share of answer, sentiment, and revenue influence; communicate progress and insights clearly to marketing leadership.
Strategy, structure, and execution
- Develop and execute a holistic organic search strategy that covers classic SEO, technical optimization, and GEO/AEO tactics tailored to AI answer engines and Google AI search experiences.
- Own site architecture recommendations, internal linking plans, and on\-page optimization for key commercial, informational, and program pages to improve crawlability, indexation, and relevance for high\-value queries.
- Design and implement AI\-friendly content structures such as Q\&A sections, FAQ modules, comparative tables, and concise summary blocks to improve extractability and citation likelihood in AI answers.
- Partner with engineering and product teams to ensure strong schema coverage and technical SEO foundations, including structured data, site performance, and other quality signals that support both SEO and GEO.
Lead content strategy and production
- Own the editorial calendar for onsite and blog content across brands, aligned to priority keyword clusters, real\-world prompts, and patient journeys from awareness through conversion.
- Translate search behavior, customer questions, forums, and market signals into high\-impact content opportunities that can win in both traditional SERPs and AI\-generated answers.
- Build and manage an AI\-enabled content workflow for ideation, outlining, drafting, optimization, and publishing while maintaining strong human review, clinical accuracy, and brand voice.
- Manage content production across internal and external resources, ensuring output is structured, accurate, scalable, and optimized for both search engines and LLMs.
Profound and AI\-search tooling
- Use Profound as a core platform for monitoring brand visibility across AI\-generated answer platforms.
- Translate Profound insights into actionable recommendations for content strategy, technical SEO, and messaging improvements that increase share\-of\-answer and brand representation in AI search.
- Connect Profound data with the broader SEO and analytics stack, including Google Search Console and GA4, to create a unified view of organic performance across traditional and AI environments.
- Continuously test and refine topics, prompts, page structures, and optimization approaches using Profound and other AI\-enabled workflows.
Test\-and\-learn and optimization
- Build and manage a structured experimentation roadmap across metadata, content formats, schema types, internal linking, and AI\-specific optimization tactics.
- Systematically refine AI\-generated content workflows, including prompt libraries, QA checklists, and model usage standards, to improve quality, speed, and compliance.
- Partner with analytics, product, engineering, and CRO teams to ensure organic and AI\-driven traffic translates into incremental business impact through landing page optimization and funnel improvements.
Cross\-functional leadership
- Operate organic search and AI visibility as a portfolio across brands, sharing learnings, defining topic territories, and helping avoid cannibalization.
- Partner with paid search, brand, lifecycle, social, PR, and affiliate teams so organic and AI strategies complement broader go\-to\-market initiatives.
- Collaborate with engineering, product, and data teams to improve tracking for AI bot traffic, AI\-influenced sessions, and AI\-assisted conversions where feasible.
- Serve as an in\-house subject matter expert for organic search and AI visibility, helping raise the bar on experimentation, process discipline, and performance management.
Qualifications
Experience
- 5–8 years in performance\-oriented SEO, organic growth, content strategy, or related search roles for B2C or e\-commerce businesses; healthcare, telehealth, or regulated category experience preferred.
- Demonstrated success driving meaningful organic traffic growth through technical SEO, on\-page optimization, content strategy, and cross\-functional execution.
- Hands\-on experience with GEO, AEO, or related optimization for AI answer engines and AI\-driven search experiences.
- Direct experience with Profound or similar AI\-search visibility platforms strongly preferred.
- Proven track record managing a high\-output editorial or content function, either in\-house or through agencies and freelancers.
Skills
- Strong expertise in technical SEO, site architecture, crawl and indexation management, schema, internal linking, and on\-page optimization.
- Strong understanding of GEO and AEO concepts, including AI extractability, answer\-first structuring, prompt mapping, AI citation tracking, and share\-of\-answer measurement.
- Advanced proficiency with SEO and analytics tools such as Google Search Console, GA4, and enterprise SEO platforms, plus AI\-search platforms like Profound.
- High fluency in using AI for marketing workflows, including research, ideation, drafting, optimization, and publishing, with rigorous editorial oversight.
- Strong analytical ability, including experimentation design, funnel analysis, and connecting organic and AI\-search metrics to business outcomes.
- Excellent communication skills and the ability to translate complex SEO, GEO, and AEO concepts into clear recommendations for cross\-functional stakeholders.
Benefits
- Medical Insurance, Dental, Vision, Whole Life, Critical Illness, Accident \& Short Term Disability Benefits
- 401(k)
- Paid Time Off
- Paid Holidays
- Maternity, Paternity and Bereavement Benefits
- Competitive Pay
- Flexible Schedule
- Relaxed "Tech Focused" Environment
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 Analyte 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $227,400.
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.
Analyte Health AI Hiring
Analyte Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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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