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About This Role
Department: Marketing
Reports To: Director of Marketing
Location: Boise, ID (Preferred) \| Remote Considered
Salary Range: $70,000–$80,000 DOE
About the Role
FiftyFlowers is building an industry\-leading Organic Search program focused on the future of search. As search continues to evolve beyond traditional Google rankings, we're investing in a strategy that combines Search Engine Optimization (SEO), AI Search Optimization (AEO), and Generative Engine Optimization (GEO) to ensure our content is discoverable wherever customers search—from Google and Google AI Overviews to ChatGPT, Perplexity, Gemini, Claude, and emerging AI\-powered search experiences.
We're looking for an SEO, AI Search \& Ecommerce Content Strategist who understands both how customers shop online and how modern search engines retrieve, summarize, and recommend content. This person will own our organic content strategy from keyword research through publication, while partnering with teams across the business to create content that is helpful, authoritative, conversion\-focused, and optimized for both traditional and AI\-powered search.
This is a hands\-on role with strategic influence. You'll work directly with the Director of Marketing to execute and continuously improve our in\-house organic search strategy while becoming the company's subject matter expert for SEO and AI Search best practices.
Key ResponsibilitiesSEO, AI Search \& Content Strategy
- Conduct keyword research, search intent analysis, competitor research, and content gap analysis using Google Search Console, Google Keyword Planner, SEMrush (or similar platforms), and AI search tools.
- Identify opportunities to grow organic traffic, revenue, and topical authority.
- Develop keyword briefs and content recommendations for new pages, blogs, buying guides, FAQs, and educational resources.
- Build content strategies that perform across Google Search, Google AI Overviews, ChatGPT, Perplexity, Gemini, Claude, and other emerging AI\-powered search platforms.
- Continuously monitor industry trends and recommend new SEO, AI Search, and content optimization opportunities.
Ecommerce Content Creation \& Optimization
- Write and optimize high\-performing ecommerce content, including:
- Collection pages
- Category pages
- Landing pages
- Buying guides
- Educational blog articles
- FAQs
- Pillar pages
- Content clusters
- Product descriptions
- Seasonal campaign content
- Create compelling titles, meta descriptions, headers, and on\-page copy that balances search performance with customer experience and conversion.
- Structure content using semantic SEO, clear information architecture, and answer\-first formatting to maximize visibility across both traditional and AI\-powered search.
Content Governance \& Editorial Leadership
- Serve as the internal SEO and AI Search expert for the Marketing team.
- Provide keyword research, content briefs, and optimization guidance to internal stakeholders creating content.
- Review, edit, humanize, and optimize AI\-generated and human\-written content before publication to ensure accuracy, brand voice, readability, and SEO quality.
- Proofread blogs, landing pages, product descriptions, collection pages, and other marketing content to ensure consistency and search optimization.
- Establish and maintain content standards, SEO best practices, and editorial guidelines across the organization.
- Ensure content follows internal linking, metadata, formatting, and structured content best practices.
Shopify \& Cross\-Functional Collaboration
- Create, edit, optimize, and publish SEO content within Shopify.
- Partner closely with Merchandising, Ecommerce, Product, Customer Experience, Web Development, and Marketing teams to improve content across the customer journey.
- Support merchandising initiatives by optimizing collection pages, category structures, seasonal campaigns, and product content.
- Collaborate with subject matter experts throughout the organization to transform business knowledge into high\-performing, search\-optimized content.
Performance \& Continuous Improvement
- Monitor rankings, organic traffic, AI visibility, click\-through rates, and content performance.
- Identify optimization opportunities using analytics and search performance data.
- Support monthly reporting and communicate insights and recommendations to leadership.
- Continuously refine content based on customer behavior, search trends, AI search evolution, and business priorities.
QualificationsRequired
- 3–5\+ years of hands\-on SEO experience, preferably within ecommerce.
- Proven experience writing SEO content for ecommerce brands, including collection pages, category pages, landing pages, buying guides, blogs, product descriptions, and educational content.
- Required experience working within Shopify, including creating and editing pages, collections, blogs, and SEO metadata.
- Strong understanding of traditional SEO, search intent, semantic SEO, internal linking, metadata optimization, and content architecture.
- Strong understanding of AI Search, Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), or a demonstrated ability to quickly adapt to emerging AI search technologies.
- Experience using Google Search Console, Google Keyword Planner, SEMrush, Ahrefs, or similar SEO platforms.
- Exceptional copywriting and editing skills with a portfolio of ecommerce writing samples.
- Experience editing and improving AI\-generated content while maintaining brand voice and quality.
- Strong organizational skills with the ability to manage multiple projects and priorities.
- Understanding of how to create high\-quality, human\-first content that avoids generic AI\-generated writing and aligns with Google's quality guidelines.
- Ability to optimize content for both traditional search engines and AI\-powered search experiences while maintaining a consistent brand voice.
Preferred
- Experience working with merchandising, ecommerce, or product teams.
- Familiarity with schema markup and technical SEO fundamentals.
- Experience with content governance, editorial standards, or style guide development.
- Experience using AI writing and research tools as part of an SEO workflow.
- Experience auditing, refreshing, and optimizing legacy content to improve search performance, relevance, and conversions.
- Experience collaborating with social media teams by repurposing SEO content for channels such as LinkedIn, Instagram, TikTok, or other digital platforms.
- Experience performing content audits and identifying opportunities to consolidate, update, or rewrite existing content to improve organic performance.
What Success Looks LikeFirst 90 Days
- Become proficient in FiftyFlowers' SEO, AI Search, and content strategy.
- Independently perform keyword research, create content briefs, optimize pages, and publish content within Shopify.
- Begin supporting cross\-functional teams with SEO guidance and content reviews.
Within 6 Months
- Own content projects from keyword research through publication.
- Establish consistent editorial standards and SEO best practices across the Marketing team.
- Improve the quality, consistency, and performance of all organic content published across the site.
Ongoing
- Increase organic traffic, rankings, organic revenue, and visibility across traditional and AI\-powered search.
- Grow FiftyFlowers' authority across weddings, DIY, floral education, events, and wholesale content.
- Build a scalable, best\-in\-class organic content program that positions FiftyFlowers as the trusted source for floral education and ecommerce content across both search engines and AI platforms.
Why Join FiftyFlowers?
This is an opportunity to help shape the future of organic search at FiftyFlowers. You'll play a foundational role in building an industry\-leading SEO and AI Search program, collaborate across multiple teams, and directly influence how millions of customers discover our brand through both traditional search engines and the next generation of AI\-powered search experiences.
Pay: $70,000\.00 \- $80,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Health insurance
- Health savings account
- Paid time off
- Parental leave
- Vision insurance
Work Location: Remote
Salary Context
This $70K-$80K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FiftyFlowers.com, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($75K) sits 65% below the category median. Disclosed range: $70K to $80K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FiftyFlowers.com AI Hiring
FiftyFlowers.com has 1 open AI role 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 $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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