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About This Role
ABOUT EXTENTEAM
Extenteam is an AI\-driven operations platform built for short\-term rental property managers, automating guest communication and daily workflows with human\-in\-the\-loop support across tens of thousands of properties.
Alongside our core STR business, we operate a multifamily property management division that generates $250,000 in monthly revenue, serving some of the largest operators in the country \- companies running thousands of units across multiple states. The work is real, the clients are large, and the processes that power this division today are largely manual. We know what works. Now we want to build the technology layer that systematizes and automates it.
This role exists to lead that effort.
THE ROLE
This is the first product and engineering hire dedicated to the multifamily division. You will own the AI and automation layer for this side of the business \- sitting at the intersection of product management and hands\-on AI system design. You are not starting from scratch. There is $250K/month of revenue, real clients, real workflows, and real operational data to work with. Your job is to turn what works manually into a product that scales.
You will be working independently at the start \- there is no dedicated engineering team from day one. You are expected to build, ship, and iterate on your own. If this work proves out, the function grows around you. That means you need to be genuinely technical: comfortable writing code, deploying pipelines, and owning the full stack of what you build.
You will design automation\-first workflows, determine where AI can own a process end\-to\-end, and define where human oversight is still required. You will work closely with Extenteam agents and client\-facing teams to surface workflow bottlenecks and translate operational reality into systems that actually work.
This role reports directly into the Head of Multifamily and is a founding position on the product and engineering function for this vertical.
WHAT YOU WILL OWN
\- Own the AI and automation product scope for the platform: discovery, definition, prioritization, delivery, and iteration \- largely independently at the start
- Write clear, unambiguous specs and documentation for the systems you build
- Run structured discovery with Extenteam agents and client\-facing teams to surface workflow bottlenecks and automation opportunities
- Maintain a prioritized backlog, balancing quick wins with meaningful product bets
\- Own the performance and iteration of AI models within your scope \- define evaluation criteria, monitor output quality, and drive continuous improvement
\- Build and maintain agentic workflows, Claude API pipelines, and MCP integrations directly \- you are not just writing specs, you are shipping
\- Lead automation\-first thinking \- every workflow you touch should ask whether a human is still needed, or whether AI can own it end\-to\-end
- Communicate progress, blockers, and decisions clearly to the Head of Multifamily and leadership
- Track adoption and outcomes; use data to inform what to build next
YOUR DAILY TOOLKIT
You should be comfortable and fast across all of these:
\- Claude Code \- this is your primary AI development interface; you live in it
\- Claude API \- pipelines, prompt chains, structured outputs, evaluation frameworks
\- Terminal, CLI, bash \- you are fluent at the command line
\- GitHub \- commits, branches, PRs, basic git hygiene
\- Railway and/or AWS \- you can deploy and manage what you build
\- SQL and database connections \- PostgreSQL, piping APIs into DBs, reading/writing data
\- REST APIs \- you can read docs and connect anything to anything
\- MCP servers and tool\-use integrations \- you have built or extended them
WHO YOU ARE
Required
- 3\-6 years of product management or builder experience, ideally at a B2B SaaS or proptech company
\- Hands\-on experience managing or iterating on AI models in a product context \- you understand prompt design, evaluation frameworks, and how to improve model performance over time
\- Deep familiarity with Claude Code \- you use it as a force multiplier, not a crutch
\- Proven ability to build agentic workflows, AI pipelines, or automation systems \- you have shipped something real, solo or as a lead
\- Strong written communication skills \- you can turn fuzzy operational problems into clear system specs
- Comfortable working in a fast\-moving, unstructured environment with no hand\-holding
- Experience shipping full product features from discovery to launch, independently or with minimal support
- Hands\-on experience designing or improving workflows, decision trees, or automation\-ready processes
- Comfort working with data, dashboards, and analytics tools to guide decisions
- Must be based in or willing to relocate to Miami, Florida or Los Angeles, California
Preferred
- Experience in property management, proptech, or multifamily real estate
- Familiarity with property management platforms (Yardi, AppFolio, Buildium, Entrata, or similar)
- Background building or managing human\-in\-the\-loop AI systems where model confidence determines when a human steps in
- Experience at a company going through a manual\-to\-AI or services\-to\-SaaS transition
- Familiarity with LLM orchestration tools or agentic workflow design beyond Claude
\- Familiarity with ClickUp, Slack, or HubSpot \- helpful but not required
KEY ATTRIBUTES
- Execution Focused: You move ideas into production quickly and consistently deliver measurable results
\- AI\-Native Builder: You do not bolt AI onto existing workflows \- you design the workflow around what AI can own, define where humans add value, and write the code to make it real
\- Domain\-Curious: You take the time to understand this space deeply \- the vocabulary, the pain points, the compliance requirements, and the people doing the work
- Systems Thinker: You understand how all parts of a product connect and design solutions that work at scale
\- Technically Fluent: You ship code. You are not waiting for an engineering team to interpret your spec \- you prototype, build pipelines, and deploy
- High Velocity: You thrive in fast\-paced environments, iterate quickly, and maintain momentum without sacrificing quality
- Ambiguity Crusher: You bring clarity to messy, unclear situations and create order that others can follow
WHAT SUCCESS LOOKS LIKE
- 30 days: Deep understanding of the product, core agent and client workflows, and the operational processes currently running manually. First pipeline or automation scoped and in flight.
- 60 days: First automation shipped and running. Regular discovery cadence with agents and client\-facing teams. AI model evaluation framework defined and in use. Clear ownership established.
- 90 days: Meaningful feature live. Visible product and AI momentum in your area. Model performance measurably improving. Data tracked and driving next priorities.
NOT A FIT IF
- You have only used Claude or ChatGPT through the UI
- You need an engineering team to execute your ideas
- You need detailed specs or close direction before you can start
- You are looking for a pure PM role with no hands\-on building
\- You are not prepared to be the first person in this function \- with everything that comes with that
BENEFITS
- Health insurance
- Paid time\-off
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 Extenteam Careers, 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.
Extenteam Careers AI Hiring
Extenteam Careers has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, 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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