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About This Role
ABOUT GREYSTAR
Greystar is a leading, fully integrated global real estate platform offering expertise in property management, investment management, development, and construction services in institutional\-quality rental housing. Headquartered in Charleston, South Carolina, Greystar manages and operates over $300 billion of real estate in more than 250 markets globally with offices throughout North America, Europe, South America, and the Asia\-Pacific region. Greystar is the largest operator of apartments in the United States, managing over 1,000,000 units/beds globally. Across its platforms, Greystar has nearly $79 billion of assets under management, including over $35 billion of development assets and over $30 billion of regulatory assets under management. Greystar was founded by Bob Faith in 1993 to become a provider of world\-class service in the rental residential real estate business. To learn more, visit www.greystar.com.
JOB DESCRIPTION SUMMARY
This position leads the performance of technical and mechanical work that ensures the physical aspects of the units, buildings, grounds, amenities, and common areas of the property meet the Company’s standards for cleanliness, appearance, safety, and overall functionality.JOB DESCRIPTION
Property Type: Lease up, Mid\-Rise
Unit Count: 240
Schedule: Sun \- Thurs, 8 am to 5 pm. On call is required with two\-week rotation
Requirements: 1 \-2 years of related experience
Housing Discount \- This position is eligible for 20% housing discount.
Commission/Bonus Eligible: Renewal bonus
*Preferred candidates will have experience with Yardi, Outlook, and Happy Property, along with lease\-up knowledge and strong vendor relationship management skills. CPO certification is preferred. Bilingual proficiency in Spanish is a plus but not required.*
1\. Leads/performs the completion process of work orders generated from resident requests for service, as well as preventative maintenance on the property by diagnosing the source or cause of the defect or problem, and making repairs in accordance with established policies, timelines, procedures, safety standards, and code requirements under the supervision of the Area Maintenance Manager.
2\. Leads/performs the completion of the “make\-ready” process to prepare vacant apartment homes for leasing and new move\-ins by completing the pre\-move\-out inspection, creating a “punch” list of maintenance work needed, scheduling vendors and contractor as needed, obtaining needed supplies and materials, completing all maintenance tasks, and inspecting completed work per the established timelines and policies under the supervision of the Area Maintenance Manager.
3\. Leads/performs the process of accessing and obtaining materials, supplies, equipment, tools, and other items from the property’s maintenance department by tracking inventory used, returning unused items to the established location, and ordering supplies under the supervision of the Area Maintenance Manager.
4\. Leads/performs the completion process for all inspections by monitoring Leonardo 247, Entrata, and all other platforms required to complete all current inspections as well as any future property or unit inspections required by code, regulation, or policy.
5\. Completes documentation and other paperwork in a timely, accurate, and complete fashion so that service requests can be appropriately documented and tracked.
6\. Leads/performs in maintaining the grounds, common areas, and amenities by picking up trash and debris, pressure\-washing breezeways and pool areas, performing general cleaning, and painting curbs and signage as needed.
7\. Supports cost\-cutting and expense control programs by fixing rather than replacing parts when possible, not being wasteful with materials and supplies, and practicing the correct use for tools and equipment.
\#LI\-JG1
The hourly range for this position is $27\.88 \- $33\.00 (Fremont).
Additional Compensation:
Many factors go into determining employee pay within the posted range including business requirements, prior experience, current skills and geographical location.
- *Corporate Positions*: In addition to the base salary, this role may be eligible to participate in a quarterly or annual bonus program based on individual and company performance.
- *Onsite Property Positions*: In addition to the base salary, this role may be eligible to participate in weekly, monthly, and/or quarterly bonus programs.
Robust Benefits Offered\*:
- Competitive Medical, Dental, Vision, and Disability \& Life insurance benefits. Low (free basic) employee Medical costs for employee\-only coverage; costs discounted after 3 and 5 years of service.
- Generous Paid Time off. All new hires start with 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays. Plus your birthday off after 1 year of service! Additional vacation accrued with tenure.
- For onsite team members, onsite housing discount at Greystar\-managed communities are available subject to discount and unit availability.
- 6\-Week Paid Sabbatical after 10 years of service (and every 5 years thereafter).
- 401(k) with Company Match up to 6% of pay after 6 months of service.
- Paid Parental Leave and lifetime Fertility Benefit reimbursement up to $10,000 (includes adoption or surrogacy).
- Employee Assistance Program.
- Critical Illness, Accident, Hospital Indemnity, Pet Insurance and Legal Plans.
- Charitable giving program and benefits.
- *Benefits offered for full\-time employees. For Union and Prevailing Wage roles, compensation and benefits may vary from the listed information above due to Collective Bargaining Agreements and/or local governing authority.*
Greystar will consider for employment qualified applicants with arrest and conviction records.
*Important Notice:* *Greystar will never request your banking details or other sensitive personal information during the interview process. Greystar does not conduct any interviews via text or messaging, and all communication will come from official Greystar email addresses (@greystar.com). If you receive suspicious requests, please report them immediately to AskHR@greystar.com.*
Salary Context
This $56K-$68K 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 Greystar, 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 ($62K) sits 63% below the category median. Disclosed range: $56K to $68K.
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.
Greystar AI Hiring
Greystar has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Houston, TX, US, Fremont, CA, US, Los Angeles, CA, US. Compensation range: $52K - $90K.
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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