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About This Role
### Xanitos is seeking a Regional Training Director for the West Region.
- This position will support Training activities in CA, WA, AZ, MT, and TX.
The Regional Training Director will oversee and coordinate all training program activities in the designated region. A successful training program requires imaginative design, detailed planning, synchronized coordination, creative presentation, and meticulous documentation. It requires innovative thinking, dynamic action, continuous and clear communication and rapid, thorough follow-up. It also requires the collaborative support of colleagues on the management team.
Essential Duties and Responsibilities:
- Develop, execute and maintain standardized training across the company include but not limited to web-based seminars, annual training, new hire orientation, printed manuals, group sessions, and training videos
- Develop, plan, coordinate, execute and document small clinic/group training in such learning fundamentals as SDS, RACE, Infection Control, Body Mechanics, Safety, Domestic Violence, Age-Specific Protocols, Hospital, and Department P&P, and any other Hospital or company-mandated training programs.
- Develop, plan, coordinate, execute, and document cross-training and remedial training programs in both one-on-one and small clinic formats.
- Develop and oversee implementation of Method of the Week and Safety Moment of the Week programs.
- Plan, coordinate and oversee training documentation completed at facilities
- Customize training documentation forms as required in accordance with Xanitos standardized programs
- Prepare monthly reports of training activity and statistics.
- Liaise with other Xanitos Regional Training Managers, Regional Directors, Vice Presidents, COO, Corporate HR and Training staff, and on-site resources to remain at "leading edge."
- Continually review existing training materials produced to determine appropriateness and relevance. Those needing to be updated should be then evaluated and updated in coordination with the other RTMs.
- Provide adequate notice to supervisors/managers of impending training exercises and coordinator relief/replacement staff as necessary.
- Assist in preparation of memory aids for staff; i.e. laminate cards for SDS, Mission, Race, etc.
- Prepare master training schedule for the region annually
- Determines training needs and requirements for the company by analyzing current training methods, conducting meetings with managers, talking with employees, and/ or administering surveys
- Conduct onsite visits to assists with training needs and audit current programs and documentation, ensuring the facility is maintaining compliance with regulatory requirements as related to EVS management and hourly training
- Develop and engage onsite training managers
- Responsible for managing, assigning, and tracking all third party professional development training programs/ workshops in conjunction with the other RTMs.
Assist in organizational communications such as company newsletters, social media, and sales material as needed to ensure clear and accurate understanding of training and developments events and resources
Qualifications:
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Ability to communicate effectively in written format and oral presentations
- Ability to multi-task and establish priorities
- Ability to maintain organization in a changing environment
- Exhibit initiative, responsibility, flexibility, and leadership
- Possess a thorough knowledge of contract administration and office procedures
- Ability to use knowledge of working environment to meet established goals and objectives
Experience:
*Training EVS Healthcare experience required.*
Ambulatory, Bilingual (English/Spanish), Floorcare, Linen, Patient Sitting (POA),Patient Transport, Previous Director, Unions, highly desired.
Education:
Bachelor’s degree or related experience and three to five years of supervisory experience. One to two years director-level experience in EVS or service-related field with high customer/client contact required. One to two years training/education experience in EVS or service-related field.
Computer and System Skills:
Excel, Learning Management Systems, Outlook, Patient Satisfaction (i.e. Press Ganey), PowerPoint, Teams, Word.
Highly proficient in the Microsoft Office Suite (Excel, PowerPoint, Word, Outlook) with the ability to create data-driven reports, presentations, and scalable training resources. Demonstrated strength in public speaking, facilitation, and delivering engaging learning experiences for both frontline teams, operational leadership, and corporate senior leadership. Preferred experience includes instructional design, learning management system (LMS) administration, development and implementation of professional development programs, succession planning, and regulatory compliance.
Travel:
Approximately 50% travel throughout the Region as well as occasional travel to additional locations.
Xanitos understands the importance of you, your family’s health and well-being, and your financial future. With that in mind, we take pride in the variety of benefit plans that are available for our employees. Please note, plans vary by location and are subject to eligibility and work hour requirements in accordance with company policy and state laws. Plans may include:
- Medical
- Dental
- Vision
- Life, Accident, and Disability Insurance
- 401K Retirement Plans
- Employee Assistance Program (EAP)
- Employee Wellness Program
- Commuter Benefits
- Shoes for Crews Reimbursement
- Paid Time off including Vacation, Sick, Holidays, Elective Holidays, Bereavement, Parental Bonding, Volunteer Day, and Jury Duty.
- Employee Discounts to Theme Parks, Theaters, Sporting Events, Movies, and More.
Xanitos, Inc. Is a management company that provides hospital housekeeping, patient transport, central laundries services, and patient observation services. It is differentiated by its patented XRO System for cleaning patient rooms, its outstanding operations management team, and by being a private company whose priority is giving top-quality service. The results are evident; at the hospitals it serves, Xanitos has improved the cleaning quality, increased HCAHPS scores, reduced the risk of HAIs, lowered bed turnaround time, and significantly reduced costs.
The expected salary range for this position ranges from $100,000 to $110,000 depending on circumstances including an applicant’s skills and qualifications, certain degrees and certifications, prior job experience, training, market data, and other relevant factors. Additional compensation may include a bonus or commission (if applicable to the position).
Salary Context
This $100K-$110K 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 Xanitos, 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. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($105K) sits 32% below the category median. Disclosed range: $100K to $110K.
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.
Xanitos AI Hiring
Xanitos has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $110K - $110K.
Location Context
AI roles in Los Angeles pay a median of $179,440 across 1,356 tracked positions. That's 6% below the national 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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