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About This Role
AI Fellow: Build, Don't Wait
12\-24\-month fellowship · Recent CS, MIS, or equivalent STEM grads · No experience required
You spent four years learning to build things. The job market would like you to spend the next two learning to be patient because you have no experience.
We disagree.
What this AI Fellow actually is
We're standing up an enterprise\-wide AI Builder Community\-a flat, cross\-functional group of people who ship AI\-native solutions for a real business. Direct access to decision\-makers and business leaders. Less process for process's sake. More building, testing, and learning. No "junior dev" purgatory.
The AI Fellows Program is the engine. We're hiring ambitious recent grads who would rather build something real than refresh their inbox.
You'll work alongside:
- Business operators who know the problem cold
- Senior engineers who'll teach you what GitHub won't
- AI practitioners, consultants, and technical experts from leading AI organizations
- Citizen developers from every corner of the company
- Other Fellows who are just as hungry as you
Together, you'll ship agents, copilots, workflow automations, voice AI, decision intelligence\-real systems that move real numbers (revenue, member experience, operating cost) for a real business.
About MASA
MASA has been a leader in emergency medical transportation coverage since 1974, supporting more than 2 million members across the United States and the Caribbean. As we continue modernizing how we operate and serve our members, we are investing in AI\-enabled workflows, automation, and operational innovation across the business.
The AI Launchpad Program reflects that commitment \- bringing together business leaders, operators, and emerging technical talent to identify practical applications for AI that improve member experience, operational efficiency, and organizational effectiveness. At MASA, you’ll have the opportunity to help shape how a growing enterprise adopts AI in real\-world business environments while supporting a mission centered on “Protecting families with compassion when others don’t.” Learn more at https://www.masaaccess.com
Why this fellowship is different
- Sponsored by MASA's executive leadership team, including direct visibility to senior leaders throughout the program.
- Opportunities to regularly present ideas, pilots, and business outcomes to executives.
- Hands\-on experience helping shape how a growing enterprise adopts AI in real\-world business environments.\[RH2]
What you'll actually do
- Build production AI solutions using Claude Code, Claude Cowork, MCP servers, RAG pipelines, and whatever drops next Tuesday.
Work across operational departments (sales, claims, member experience, ops, cyber, finance) so you learn how a real business runs* \-not just how code compiles.
- Prototype fast, demo monthly, ship continuously.
Contribute to an internal AI marketplace where your work gets reused across the enterprise* \-your name on assets the whole company uses.
- Participate in hackathons, demo days, and a builder culture that treats experimentation as the job, not a side quest.
The mindset we're hiring for
This role is not about how much you already know. It's about how you think. We're looking for people who:
- Learn faster than the docs update
- Treat AI as a teammate, not a threat
- Have built side projects that taught them more than their degree
- Would rather fix the problem than file the ticket
- See process as something to question, not worship
- Take feedback like oxygen
- Get genuinely curious about how a business works
- Default to shipping over debating
If you've ever spent a weekend building something nobody asked for because you wanted to see if it would work\-keep reading.
What we *don't* require
- "Years of experience" (you graduated last semester)
- A CS degree from a specific school
- Specific framework knowledge
- Prior internships
- A polished LinkedIn
- A perfect GPA
What we *do* require
- A recent degree in CS, computer engineering, math, data science, or a related technical field.
- Receipts. A GitHub, a side project, a Hugging Face space, a hackathon submission, a Discord bot, a Chrome extension you built for fun\-anything that shows you actually build, not just study.
- Real fluency with AI tools as daily collaborators (not just "I've used ChatGPT").
- Curiosity about the business, not just the code.
- The ability to disagree, push back, propose, and operate without handholding.
- Authorization to work in the United States.
The program
- A Structured 12\-24\-month fellowship, providing hands\-on experience implementing AI solutions across multiple business functions and operational environments.
Embedded in real business teams from week one* \-no "training sandbox" for six months.
- Full salary, full benefits, real role from day one.
- Demo Days, quarterly hackathons, internal AI Innovation Fund for your wildest ideas.
Location This is a full\-time, hybrid position based in Greater Fort Lauderdale, FL, with remote work arrangements considered for exceptional candidates.
Compensation \& benefits We offer competitive compensation and a comprehensive benefits package that includes medical, dental, and vision insurance; a 401(k) plan with company matching contributions; company\-paid short\-term disability, long\-term disability, and life/AD\&D insurance; generous paid time off, personal days, and company\-observed holidays. You'll also receive a complimentary MASA Emergent Plus membership for you and your family while enjoying a professional and friendly culture united by a clear mission: *Protecting families with compassion when others don't.*
Start date Great builders shouldn't have to wait for a cohort start date. We hire AI Fellows on a rolling basis.
How to apply
Skip the 2\-page cover letter. Share
- Your resume (one page is fine).
- A link to one thing you've built with AI\-anything\-and a short note (200 words max) on what you learned making it.
- A 60\-second video (Loom, Zoom, Teams, Vidyard, or similar) telling us why you're a great fit for this role and why this opportunity interests you. Please ensure the video can be viewed without requiring access requests.
We read everything. We respond to everyone.
*Questions? Email us at [email protected]*
*NOTE: Any pay range listed for this position is an estimate by the job board and may not reflect the actual compensation.*
*MASA is an equal opportunity employer. We hire for mindset and build the rest.*
\#LI\-RH1 \#LI\-HYBRID \#LI\-REMOTE \#corpjobs
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 MASA, 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.
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.
MASA AI Hiring
MASA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Lauderdale, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 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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