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About This Role
#### Company: PARAKEET COMMUNITIES, LLC
Location: Potomac, MD (with regular travel to Miami, FL)
Job Type: Full\-Time, Exempt
Salary: $100,000 \- $150,000 a year \+ Annual Bonus
Reports To: Company Co\-Founders
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#### WHO WE ARE
Parakeet Communities is a rapidly expanding real estate investment company specializing in Manufactured Housing Communities and RV Resorts across the United States. We acquire, upgrade, and operate communities that deliver attainable housing solutions and exceptional experiences for our residents and investors alike.
We are a lean, fast\-moving team that aggressively adopts AI across acquisitions, asset management, operations, and reporting. We are now building proprietary tools and internal software—and we are seeking the right innovator to own this frontier. If you have a "Yes" attitude, thrive in a forward\-thinking environment, and want a sense of purpose beyond your next paycheck, we encourage you to apply and "Join the Flock!"
#### THE ROLE
This is not a traditional, siloed departmental position. You will not have a fixed queue of repetitive tasks. Instead, you will operate as your own standalone division, reporting directly to the Co\-Founders. You will act as the vital connective tissue between deep AI engineering capabilities and the team running the business operations.
The Core Problem You Will Solve: Our leadership team deeply understands our business and is rapidly embracing AI, but we are only leveraging a fraction of its potential. We are developing AI\-driven tools and systems at a pace that outstrips our current ability to standardize, optimize, and scale them effectively. You will bridge this gap.
#### KEY RESPONSIBILITIES
- Tool Development: Collaborate closely with Founders and Department Heads to turn strategic business priorities into practical, productiongrade tools and internal software.
- System Hardening: Audit and secure internal builds. Identify fragile code, hidden dependencies, and "Frankenstein" structural risks that break silently. Make our AIbuilt systems auditable, resilient, and trustworthy.
- Workflow Optimization: Improve the team’s utilization of cuttingedge AI tools (including Claude, Claude Code, custom agents, and supporting infrastructure) to supercharge employee productivity.
- Agentic AI Expansion: Design, scale, and build out robust Agentic AI workflows across various business units.
- InSourcing: Transition outsourced projects (such as website development and management) inhouse when practical and scalable.
- Executive Collaboration: Attend and contribute to EOS (Entrepreneurial Operating System) and Leadership calls as a junior member of the Executive Team.
#### QUALIFICATIONS
1\. Experience: 3–5 years of relevant software engineering or technical analysis experience (flexible for the right candidate).
2\. Technical Depth: A strong software engineering/coding background. You must understand code at a structural level—not just surface\-level prompt engineering.
3\. AI Expertise: Hands\-on experience building, testing, and scaling Agentic AI workflows.
4\. Business Acumen: Experience working within a startup or a fast\-paced, real\-world business environment.
5\. Communication Skills: An exceptional communicator who can seamlessly bridge the gap between deep technical environments and non\-technical senior leaders.
6\. Autonomy: Highly comfortable being handed a strategic direction rather than a rigid specification document, with the drive to turn ideas into functional reality.
7\. Travel: Willingness and ability to travel to Miami, FL on a regular basis.
#### WHAT WE OFFER
Competitive Base Salary ($100,000 \- $150,000\) \+ Annual Performance Bonus
Comprehensive Healthcare Benefits (Medical, Dental, Vision)
401(k) Retirement Plan with Company Match
120 Hours of Paid Time Off (PTO) with up to one week of annual carryover
A collaborative, forward\-thinking, and technologically advanced work culture
#### Equal Opportunity Employer
Parakeet Communities LLC is an equal employment opportunity employer. Employment and advancement opportunities are available to all individuals on an at\-will basis, regardless of their race, color, national origin, religion, ancestry, citizenship status, military or veteran status, sex, sexual orientation, gender identity or expression, age, marital status, family responsibilities, pregnancy, disability, genetic information, protective hairstyle, or any other characteristic protected by applicable federal, state, or local law.
#### About Parakeet Communities LLC
Parakeet Communities is a rapidly expanding real estate investment company that purchases, owns, and operates Manufactured Housing Communities and RV parks. Our mission is to cultivate communities that change the perception of mobile residencies and designate this style of living as the “future of real estate in America.” We encourage opportunities for growth \& development by providing our employees with the resources to continually strive to optimize success. We are committed to a safe, sustainable, and pleasant work environment. Our brand thrives on the dedication to our communities and the way we treat our customers. This includes following safety provisions, complying with state \& federal law, and approaching our responsibilities ethically. We turn challenges into wins without compromising professionalism. If you have a "Yes" attitude and want to contribute to the success of Parakeet's rapid expansion, we encourage you to apply for the listed position and join the "Flock!"
Salary Context
This $100K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Parakeet Communities, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($125K) sits 43% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Parakeet Communities AI Hiring
Parakeet Communities has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Potomac, MD, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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