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About This Role
Guardian has an immediate need for a Full\-Time Maintenance Lead/Supervisorto join our team at Marion Court!
Marion Court is a 35\-unit apartment community in Bremerton, WA.
For this position, we are seeking a self\-starter who enjoys leading/supervising others and excels in a fast\-paced, hands on environment. The ideal candidate will be highly organized, professional and communicative; andhave a minimum of two (2\) years’ experience in multifamily property maintenance, building maintenance, or similar experience plus specific knowledge of building systems and the ability to work independently.
A Maintenance Lead's primary role is to manage site maintenance and other maintenance and housekeeping staff, and to support the Community Manager by ensuring site safety and completion of required property maintenance necessary to maintain excellent curb appeal and the overall appearance and quality of the property. This will include prioritizing and processing work orders which may include both urgent and routine maintenance of property and apartment units.
This position is responsible for ensuring that all maintenance work is done in compliance with OSHA regulations, and Guardian policies and procedures. A Maintenance Supervisor is also responsible for coordinating with other property staff and vendors to provide outstanding customer service to the residents, provide well\-timed and proactive property maintenance, safeguard the good financial health and stability of the property, and complete all required paperwork and reports on time.
Schedule: 35\-40 hours/week, Mon \- Fri 8:00 AM \- 4:30 PM.
Compensation: $26\.00 \- $28\.00/hr \+ bonus potential!
Housing provided: Common Area Unit optional.
Benefits: Medical/Vision/Prescription Insurance, Dental Insurance, Medical/Dependent Care FSA, Life/AD\&D Insurance, Employer\-matched 401\-K, 10 days of Vacation Time per year, 1 hour of Sick Time for every 30 hours worked, 7 Paid Holidays \+ 1 Birthday Holiday, Employee Assistance Program.
Guardian provides a culture in which employees are encouraged to learn new skills and develop their existing strengths. Upward mobility is a reality here! We provide ample training and opportunity for anyone who desires to take their property management career to the next level! Interested in growing along with one of the leading property management companies in the Northwest?
This could be the perfect opportunity for you!
Minimum Qualification Requirements
The requirements listed below are representative, but not exclusive of the knowledge, skill and/or ability required.
- High school diploma or GED.
- At least two (2\) years’ working experience as a Property Maintenance Technician.
- Basic knowledge of building trades, such as plumbing, electric, and carpentry.
- Ability to lift a minimum of 50 lbs.
- Comfortable on ladders at heights of 15 feet.
- Excellent attention to detail and organizational skills.
- Ability to speak, read and write in English.
- Ability to communicate effectively and in a timely manner, both verbally and in writing.
- Able to complete online and in\-person company training requirements for this position within pre\-determined deadlines and attend other training sessions as required.
- Possess basic computer\-literacy and willingness to complete regular tasks using standard computer or web\-based technology.
- Willingness to be on\-call on a rotating basis for occasional after hours and weekend emergencies
Preferred Qualifications
The qualifications listed below are representative, but not exclusive of the knowledge, skill and/or ability required.
- CAMT certification
- OSHA 10 certification
- Knowledge of UPS/REAC requirements
- Prior supervisory experience
- Own or have access to a reliable and properly insured vehicle for use in business transportation needs, possess valid driver’s license.
- If the property has a pool, CPO (Certified Pool Operator) certification may be required according to the regulations of the property’s State.
Essential Functions
To perform this job successfully, an individual must be able to perform each essential function satisfactorily with or without reasonable accommodations.
- Always represent Guardian in a positive and professional manner.
- Support Community Managers efforts to ensure that the company’s efforts fully meet and exceed property management obligations.
- Manage the schedules and work loads of all other Maintenance Staff to coordinate requests for repairs and maintenance.
- Maintain the property so it follows all local, state, and federal laws (fire, backflow, OSHA, etc.).
- Alert Community Manager of any potential liabilities or repairs. Identify property problems and repairs needed in the areas of structural, electrical, plumbing, and appliances.
- Purchase maintenance supplies and adhere to property maintenance budget.
- Under the direction of the Community Manager and (if applicable) with the assistance of other Maintenance Staff, respond to all service requests and complete all Work Orders and Apartment Turnovers in a timely and professional manner in accordance with Guardian’s policies and procedures.
- Under direction of the Community Manager or Portfolio Manager, gather bids property improvement.
- Keep accurate records of current space conditions including roof, HVAC, utilities, and special circumstances. Keep the model unit in condition to show and make sure that the office, maintenance work area, grounds and common areas are always neat and clean.
- Regularly inspect property and vacant apartments and coordinate the work and schedules of property vendors.
- Responsible for scheduling emergency coverage and making sure all office staff have access to this information.
- Required to carry a property cell phone and be on call for emergencies after business hours and on weekends, on a rotating basis with other staff.
- Communicate with the Community Manager about any issues, or resident questions or concerns brought directly to the Maintenance Supervisor.
- Perform maintenance or coordinate maintenance of pools, spas, and pool areas (if applicable).
- Always follow confidentiality guidelines for all resident, property, owner, and Guardian information.
- Accurately record time worked, adhere to time keeping guidelines daily.
- Liaison with corporate departments to provide a team approach to the management of the property.
- Establish and maintain collaborative working relationships between departments and with coworkers.
- Constantly strive for improvements in work process and results to better meet resident needs, ownership goals and objectives, and company standards and expectations.
- Other duties as assigned by the Community Manager or Portfolio Manager.
Guardian – Company Description
Guardian is a developer, owner, and operator of multifamily properties, providing innovative real estate solutions dedicated to community and housing for all. Based in the Pacific Northwest, our customer\-focused team is committed to supporting and lifting the communities we serve. Since 2002, Guardian has developed or acquired 14,600 multifamily units and 350,000 square feet of commercial space. With more than 500 team members, our management portfolio consists of 150 communities across five states.
The Guardian Experience – Our People
Property Management is our strength. People have always been our passion. We are proud to have been recognized through employee surveys as a Top Workplace employer in 2022 and 2023\. Our team is, and has always been, actively engaged in superior real estate management and enhancing the communities in which we live and work. The company is also an active contributor to a variety of housing and education causes, and in 2023, Guardian was named the Top Philanthropic organization for medium\-size companies in its headquarters region. In this economy, companies come and go. As a third\-generation, family owned firm, Guardian’s growth and stability has evolved the company into a leading Pacific Northwest real estate management and investment firm.
Guardian Offers
In addition to competitive salaries, 401(k), Vacation, Sick Time, and paid Holidays, we offer a culture in which individuals work and participate in collaborative team environments and are encouraged to continue to grow both professionally and personally.
AAP/EEO Statement
This institution is an equal opportunity provider and employer.
If you wish to file a Civil Rights program complaint of discrimination, complete the USDA Program Discrimination Complaint Form, found online at http://www.ascr.usda.gov/complaint\_filing\_cust.html, or at any USDA office, or call (866\)632\-9992 to request the form. You may also write a letter containing all of the information requested in the form. Send your completed complaint form or letter to us by mail at U.S. Department of Agriculture, Director, Office of Adjudication, 1400 Independence Avenue, S.W., Washington, D.C. 20250\-9410, or by fax at (202\) 690\-7442\.
Salary Context
This $54K-$58K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Guardian, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($56K) sits 66% below the category median. Disclosed range: $54K to $58K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Guardian AI Hiring
Guardian has 30 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Vancouver, WA, US, Portland, OR, US, Beaverton, OR, US. Compensation range: $47K - $70K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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 (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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