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About This Role
ABOUT THE ROLE
I’m the founder of OpenVet, an AI\-first veterinary technology company. I’m looking for an unusually capable person to work beside me in Fort Lauderdale and become my operational right hand.
This is not a traditional administrative role. You’ll take whole projects from a rough idea to a finished result—researching unfamiliar subjects, creating the plan, coordinating people, solving problems, and closing every loop. One week you may organize an industry conference; the next you may research a new market, improve an AI workflow, coordinate a product launch, hire a contractor, or untangle an operational problem.
The right person is wickedly resourceful, writes exceptionally well, learns faster than most people, and uses AI as naturally as other people use Google.
WHAT YOU’LL OWN
- Take ambiguous projects from “we should do this” to done, with minimal supervision.
- Research unfamiliar topics, synthesize the answer, recommend a course of action, and execute it.
- Plan conferences, events, travel, meetings, and other complex logistics end to end.
- Build and run AI\-powered workflows using Claude, ChatGPT, research tools, automations, and whatever else gets the job done.
- Turn conversations into plans, owners, deadlines, and completed deliverables.
- Coordinate vendors, candidates, contractors, partners, and internal team members.
- Draft emails, briefs, proposals, presentations, and other communications in the founder’s voice.
- Create simple operating systems so recurring work becomes organized, measurable, and easier.
- Protect the founder’s attention by handling decisions you can make and clearly framing the few that require him.
- Handle executive\-assistant work when needed—calendar, inbox, travel, follow\-up—but grow far beyond it.
WHAT SUCCESS LOOKS LIKE
After 90 days, I can hand you an important, messy project with very little instruction and trust that you will figure it out, keep me informed without being chased, and deliver an excellent result.
YOU MAY BE A GREAT FIT IF
- You have exceptional judgment, curiosity, follow\-through, and written communication.
- You are high\-agency: when you hit a wall, you find another door.
- You use Claude or ChatGPT every day for real work and understand both their power and their limitations.
- You can move between strategy and details without ego.
- You create clarity from chaos and notice what everyone else missed.
- You are comfortable working directly with a demanding, fast\-moving founder.
- You are discreet with sensitive business, financial, and personal information.
- You have led projects in a startup, operations, consulting, executive support, events, marketing, research, or a similarly broad environment.
Formal credentials matter less than demonstrated intelligence, resourcefulness, and ownership. If your experience is unconventional but your work is exceptional, apply.
LOGISTICS
- Full\-time.
- On\-site in Fort Lauderdale, Florida. This is an in\-person role working closely with the founder, not a remote position.
- Occasional travel, including industry conferences.
- Compensation range shown is based on experience and demonstrated ability, with room for performance\-based growth.
HOW TO APPLY
Please include a short note answering these questions. Generic or AI\-generated answers that do not contain specific evidence will not be reviewed.
1\. What is the most impressive thing you have built, organized, or solved using AI in the last 90 days? Include enough detail that we can understand exactly what you did.
2\. Tell me about a project you were given with little direction. How did you turn it into a finished result?
3\. Imagine you have 30 days to organize OpenVet’s presence at a major veterinary conference. What are your first ten actions?
4\. What is one system, workflow, or organization you improved without being asked?
5\. Share one work sample—a document, project, analysis, automation, event, or other artifact—that demonstrates how you think.
Bonus: include a prompt or AI workflow you are proud of and explain what it accomplishes.
Pay: $65,000\.00 \- $90,000\.00 per year
Benefits:
- Flexible schedule
Application Question(s):
- Describe the most impressive project you have built, organized, or solved using AI in the last 90 days. What was the goal, what did you personally do, which tools did you use, and what measurable result did you achieve?
- Tell us about a project you were given with little direction. How did you define the outcome, create the plan, handle obstacles, and drive it to completion?
- This is a full\-time, in\-person role in Fort Lauderdale. Are you able to work on\-site consistently? If you would need to relocate, explain your realistic timeline.
Work Location: In person
Salary Context
This $65K-$90K 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 Openvet, 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. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($77K) sits 64% below the category median. Disclosed range: $65K to $90K.
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.
Openvet AI Hiring
Openvet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Lauderdale, FL, US. Compensation range: $90K - $90K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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