Interested in this AI/ML Engineer role at Tell Me More?
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About This Role
Role type: Contractor, long-term contractor, contractor-to-full-time, or full-time
Level: Emerging, mid-level, or senior depending on experience
Location: San Juan, Puerto Rico preferred; remote considered for exceptional candidates
Path: Potential path toward senior content strategy, client strategy, editorial leadership, and long-term partnership opportunities
Write stories that help people become who they are trying to become.
People live out the life stories they are narrating to themselves — for better or worse.
TELL ME MORE® helps brands understand those narratives, enter them honestly, and shape them toward positive change. When we do that well, people make better decisions, brands become more useful, and our clients’ businesses grow.
That is why we call our writers Storytellers.
Most articles on the web are dry remixes of other articles, written primarily for search engines. For many marketers, humans are treated as “traffic” moving through “funnels” for the sole purpose of “monetization.”
We do not believe that is good marketing.
We also do not believe it produces very good writing.
At TELL ME MORE, SEO matters. AEO matters. LLM visibility matters. Structure matters. Search intent matters. But great writing does more than satisfy an algorithm. It enters the narrative a real person is already telling themselves and helps them see a better next step.
That requires intelligence, empathy, strategy, artistry, research, discipline, and craft.
It also requires humility. Writing is a team sport. You will work with editors, strategists, clients, subject-matter experts, designers, AI tools, and other collaborators. You have to be willing to receive direct feedback, revise deeply, sharpen your thinking, and keep improving.
We are not looking for content assemblers.
We are looking for serious writers who want to become great Storytellers.
What you will do
As a Storyteller at TELL ME MORE, you may help create:
- Long-form articles that are useful, persuasive, and genuinely worth reading.
- SEO and AEO content that satisfies search intent without sounding like it was written for robots.
- Articles based on expert interviews, transcripts, research, client knowledge, and original strategic thinking.
- Thought leadership for brands, executives, founders, schools, service companies, and growth-stage organizations.
- Case studies that reveal the human and strategic story behind the result.
- Website copy, landing pages, service pages, and sales narratives.
- Video scripts, podcast scripts, YouTube outlines, and social video concepts.
- Content briefs, outlines, interview questions, and research summaries.
- Messaging tied to positioning, brand strategy, recruiting, sales, and customer education.
- AI-assisted drafts, research maps, content expansions, and editorial improvements.
You will also help us think.
That may mean reading a client’s website, reviewing competitor content, analyzing a transcript, studying search results, using AI to explore a topic, and then coming back with a clear point of view:
“Here is what people are really trying to understand. Here is the story everyone else is telling. Here is the deeper story we should tell instead.”
That is the work.
About storytelling as content writing
Most web writing is trapped inside the wrong question: “how do we rank for this keyword?”
That question has its place. But it is not enough.
The better questions are:
“What is this person trying to figure out?”
“What fear, desire, confusion, or hope brought them here?”
“What story are they already telling themselves?”
“What would help them move forward?”
“What does the brand know that can genuinely help?”
“What structure will make this page useful in the context where someone experiences it?”
“How do we make this piece strong enough for search engines, LLMs, clients, and actual humans?”
Writing this way is harder than ordinary content writing.
It requires you to understand the reader, the brand, the business, the search environment, the page format, the client’s expertise, and the emotional logic of the decision being made.
It also requires range. A Storyteller may need to write about culinary school, construction, career change, education, entrepreneurship, sales, leadership, health, technology, or a topic they have never encountered before.
The job is not to pretend to be an expert.
The job is to become a careful listener, a disciplined researcher, a clear thinker, and a worthy translator of expertise.
How we use AI
We are an AI-first company, but we are not interested in generic AI writing.
AI can help with research, synthesis, ideation, outlining, alternate angles, competitive analysis, structure, editing, and expansion. It can help us move faster and think more broadly.
But AI cannot replace taste.
It cannot replace judgment.
It cannot replace lived experience.
It cannot replace the writer’s responsibility to know whether the piece is actually good.
We expect Storytellers to use tools like Claude, GPT, Perplexity, NotebookLM, Gemini, and emerging AI platforms intelligently. We also expect them to know when AI is producing bland, false, generic, or strategically weak work.
The goal is not to write “AI content.”
The goal is to use AI to help create writing that is more thoughtful, more useful, more human, and more effective.
You may be a fit if
- You are a strong writer who cares about sentences.
- You are curious about people and why they make decisions.
- You understand that marketing is not just persuasion, but meaning-making.
- You can write clearly without flattening everything into bland corporate language.
- You are comfortable doing research.
- You can synthesize interviews, transcripts, notes, and messy source material.
- You enjoy making complex ideas easier to understand.
- You can write for both humans and search engines without sacrificing either.
- You are interested in narrative identity, psychology, storytelling, branding, strategy, and behavior change.
- You are willing to be edited.
- You can revise without becoming precious.
- You can work inside a team.
- You are excited about AI and already experimenting with it in your writing process.
- You want to keep getting better over years, not just complete assignments.
You are probably not a fit if
This role is probably not right for you if:
- You mainly want to produce quick SEO articles from keyword briefs.
- You rely heavily on AI without improving the work.
- You dislike research.
- You dislike revision.
- You resist editorial feedback.
- You are defensive about your drafts.
- You do not enjoy working with strategists, editors, clients, or subject-matter experts.
- You think good writing is mostly about sounding clever.
- You are careless with facts.
- You are not interested in marketing strategy.
- You think readers are just traffic.
- You are unwilling to learn how search engines, LLMs, and web pages shape the way people find and experience writing.
Skills and experience we value
You do not need to have every skill below, but useful experience may include:
- Long-form content writing
- SEO writing
- AEO / AI search content
- Journalism
- Interview-based writing
- Thought leadership
- Case studies
- Brand storytelling
- UX writing
- Website copywriting
- Video scripting
- Podcast or narrative audio writing
- Research-heavy writing
- Editing
- Content strategy
- Marketing strategy
- Psychology, humanities, education, business, or communications
We care less about whether you have followed a traditional writing career path and more about whether you can think, write, learn, and improve.
The most important thing is this:
You can take a real person’s question, fear, hope, or ambition seriously — and write something that helps.
What success looks like
In the first few months, success may look like:
- You understand TMM’s approach to storytelling, strategy, SEO, AEO, and AI-assisted writing.
- You create strong outlines that show clear thinking.
- You produce drafts that need editing, but not rescuing.
- You use AI to improve your research and thinking without letting it flatten your voice.
- You respond well to editorial feedback.
- You can synthesize client expertise into useful, readable content.
- You can explain why a piece is structured the way it is.
- You meet deadlines and communicate clearly.
Over time, success looks like:
- You become trusted with more complex assignments.
- You can lead interviews with subject-matter experts.
- You can develop content angles, not just execute assigned topics.
- You can help shape a client’s content strategy.
- You can write pieces that perform in search while also strengthening the brand.
- You can mentor other writers.
- You help TMM raise the standard for what web content can be.
How to apply
Send an email to [email protected] with:
- Your CV, résumé, or LinkedIn profile.
- Links to one or two articles, essays, scripts, case studies, or other writing samples that represent your best work.
- A short note explaining why this role interests you.
- A 3-minute Loom video answering this prompt:
“Tell us about a writing project that makes you proud. What was the goal, what made it difficult, what steps did you take, and what was the outcome?”
- A written response to this prompt:
“Pick a piece of web content from any brand that you think is unusually good. Explain why it works as writing, as strategy, and as marketing.”
- A brief explanation of how you currently use AI in your writing or research process.
If you are not familiar with Loom, you can sign up for free at loom.com.
We may also ask finalists to complete a paid writing exercise.
We require a reference check and a DISC Personality Assessment to better understand working style, communication preferences, and team fit.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Tell Me More, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Tell Me More AI Hiring
Tell Me More has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Juan, PR, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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