Interested in this AI/ML Engineer role at Every Little Letter, LLC?
Apply Now →About This Role
We are looking for an experienced Performance Marketing Specialist to take hands-on ownership of our digital customer acquisition, with an immediate focus on Google Ads, Google Shopping, e-commerce performance and emerging AI-driven search.
This is not a general marketing or social media role. We need someone who understands the numbers, can work directly inside the platforms and is interested in how consumers are increasingly discovering products through Google AI results, ChatGPT, Perplexity and other AI-powered search experiences.
What You’ll Do
- Manage and optimize Google Ads campaigns, including Search, Shopping and Performance Max
- Improve ROAS, customer acquisition cost and overall paid marketing efficiency
- Manage Google Merchant Center and product feeds
- Analyze performance through Google Ads, GA4 and Shopify
- Ensure conversion tracking and attribution are working correctly
- Build and test campaigns around products, seasonal events and promotions
- Identify opportunities to improve conversion rates, targeting, bidding and campaign structure
- Help strengthen how Every Little Something and its products appear across traditional and AI-powered search
- Monitor changes in Google AI search, ChatGPT, Perplexity and other emerging discovery platforms
- Recommend improvements to product data, site content, structured information and other signals that can improve visibility in AI-generated search results
- Identify new opportunities as paid search, organic search and AI-driven product discovery continue to converge
- Provide clear reporting on what is working, what isn’t and what should change
What We’re Looking For
- Direct experience managing Google Ads for an e-commerce business
- Strong experience with Google Shopping and Performance Max
- Experience with Shopify, GA4 and Google Merchant Center
- Strong understanding of ROAS, CAC, conversion rate and e-commerce economics
- Comfortable working independently and directly inside advertising and analytics platforms
- Familiarity with SEO and how e-commerce content is structured for search
- An active interest in AI search, generative search and the changing way consumers discover brands and products online
- Able to test new ideas and translate emerging technology into practical business actions
- Experience with retail or product-driven seasonal campaigns is a plus
- Meta Ads experience is a plus, but Google expertise is the priority
Compensation & Schedule
$40–$50 per hour, based on experience.
We expect the role to average approximately 5–10 hours per week, with additional hours potentially available during major campaigns and seasonal periods.
Additional performance-based compensation may be available based on measurable improvements in profitable customer acquisition and digital performance.
What We Want to See
When applying, please include a brief example of an e-commerce account you have personally managed, including:
- Approximate monthly ad spend
- The primary campaigns you managed
- ROAS, CPA or other key performance metrics
- What you changed
- The resulting improvement
We would also like to hear how you think AI-powered search will change e-commerce customer acquisition over the next two to three years and what brands should be doing about it now.
We care more about demonstrated results, curiosity and practical execution than a long marketing résumé.
About Every Little Something
The Shop by Every Little Something is a curated fine gifts and paper shop specializing in tangible goods, distinctive stationery, and thoughtfully selected pieces for the home, office, and everyday life. Founded in 2014, Every Little Something now operates two retail locations in historic Middleburg and Leesburg, Virginia, both within an hour of Washington, D.C., and an online shop available globally.
The Shop features exclusive creations by artist and designer Nicole Catherine, a graduate of the Savannah College of Art and Design (SCAD), whose training in graphic design and color theory informs the brand’s distinctive aesthetic. Offerings include custom wedding invitations, personal stationery, journals, children’s writing and activity books, fine pens, gifts, and home and office décor. Visit us online at shop.everylittlesomething.com
Job Type: Part-time
Pay: $40.00 - $50.00 per hour
Benefits:
- Employee discount
Application Question(s):
- After reviewing our website, what stood out to you most about Every Little Something and how do you see yourself contributing to our mission? www.shop.everylittlesomething.com
Work Location: Remote
Salary Context
This $83K-$104K 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 Every Little Letter, LLC, 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 $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 ($93K) sits 56% below the category median. Disclosed range: $83K to $104K.
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.
Every Little Letter, LLC AI Hiring
Every Little Letter, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $104K - $104K.
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.
Frequently Asked Questions
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