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About This Role
Maintenance Technician
Job Description
Thrive Facilities is built upon a shared passion - a devotion to create meaningful relationships. The kind that make us better people, that fill our lives with authenticity. We are inspired by our work, by our ability to create for those looking for community - for a place to call home, for neighbors to build real and lasting friendships with, for a place to create memories for a lifetime.
POSITION SUMMARY
The Maintenance Technician's (Facilities Technician) responsibility is to preserve and improve the condition and functionality of our communities. The Maintenance Technician will perform maintenance tasks of great variety such as cleanouts, patching, painting, plumbing repairs, electrical repairs, HVAC troubleshooting and maintenance, groundskeeping etc.
REPORTS TO: SVP of Facilities, Facilities Manager, Community Manager
RESPONSIBILITIES, EXPECTATIONS, AND DUTIES
Duties will include, but are not limited to, the following:
- Prioritizes and/or completes maintenance work and maintenance service requests, to include, but not limited to grounds, cleaning, specific carpentry, plumbing, painting, electrical, HVAC, masonry and other general maintenance in accordance with Company standards and local city, state and federal building codes when applicable.
- Executes the Make-Ready/Turn process.
- Coordinates with other employees and vendor/contractors to ensure quality, completeness, and compliance with company standards.
- Maintain daily upkeep of common areas and community buildings, including inclement weather care of snow removal by applying ice melt on sidewalks, steps, driveways, and parking lot areas of the property.
- Maintain grounds and common areas and keep them free of trash and debris.
- Inspects the physical apartment site identifying all areas in need of immediate or future repairs and maintenance and provides an inspection report to the Community Manager for review.
- Performs other duties as assigned. Actual job duties/responsibilities may vary depending on community size.
PERFORMANCE OBJECTIVES
Include, but not limited to:
- Service Request Resolution and Timeliness
- Make Ready Process Compliance and Quality
- Customer Service Surveys
- Property Scorecards
- Budget Compliance
SKILLS, EDUCATION AND EXPERIENCE
- Proven experience as maintenance technician.
- Basic understanding of electrical, plumbing, heating, cooling, and hydraulic and other systems.
- Knowledge of general maintenance processes and methods.
- Working knowledge of tools, common appliances, and devices.
- CPO (Certified Pool Operator) preferred.
- Manual dexterity and problem-solving skills.
- Good physical condition and strength with a willingness to work overtime, if needed.
- High school diploma or equivalent; Certificate in HVAC, building maintenance technology or relevant field will be a plus.
WORK ENVIRONMENT
The Maintenance Technician works on-site at an apartment community and interfaces with external/internal customers, residents, and vendors on a regular basis. The position work schedule varies depending on the property. Must be available to work overtime as needed and work on-call schedule. This individual should be flexible and readily available depending on the needs of the property.
PHYSICAL REQUIREMENTS
The Maintenance Technician's physical condition must be sufficient for the consistent and successful completion of the specific responsibilities defined for this position and for his/her performance to be in complete conformance with all professional standards defined for this position. While performing the duties of this job, the employee is frequently required to stand, walk, sit, use hands, reach with hands and arms, stoop, kneel, crouch, or crawl. May be required to lift and/or move up to 50 pounds and operate power tools. Must be able to occasionally drive during work.
BENEFITS
- Full time employment, bi-weekly pay schedule
- No after-hours or on-call emergency coverage required!
- Paid $23-28 per hour
- Benefits which include health, dental and vision insurances, 401K with match, generous PTO, apartment discounts, and free gym membership
*#LI-P1*
Salary Context
This $47K-$58K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Thrive property management, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($53K) sits 66% below the category median. Disclosed range: $47K to $58K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Thrive property management AI Hiring
Thrive property management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US. Compensation range: $58K - $58K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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