Interested in this AI/ML Engineer role at Gersh Autism?
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About This Role
For over 30 years, Gersh Autism has empowered students on the autism spectrum to reach their full potential. Serving individuals aged 5-21 across the U.S. and Puerto Rico, our network of K-12 schools provides personalized education tailored to each student’s unique learning style. We offer comprehensive therapeutic services—speech, physical, occupational therapy, and more—along with vocational training for older students. Our dedicated team collaborates with families to ensure academic, social, and emotional growth, preparing students for success beyond the classroom.
At Gersh Autism, we are committed to helping individuals on the autism spectrum lead fulfilling, independent lives.
The Maintenance Supervisor is responsible for maintaining and overseeing facilities operations across multiple locations, ensuring all campuses are safe, clean, organized, and well-maintained. This role supervises maintenance staff, coordinates day-to-day operations, responds to emergencies, performs maintenance duties, and supports new location openings while maintaining a student-centered, safety-first environment.
Please note: This position requires travel to multiple school campuses.
Duties and Responsibilities
- Supervise and manage a maintenance team of up to 10 employees across multiple locations
- Oversee daily maintenance operations to ensure timeliness, efficiency, and quality of work
- Maintain, service, and monitor all company equipment, machinery, and building systems
- Ensure upkeep, safety, and appearance of building interiors, exteriors, grounds, walkways, driveways, and parking areas
- Conduct regular site inspections and promptly address or report safety, maintenance, and compliance issues
- Coordinate snow removal, salting, and seasonal maintenance needs
- Ensure all boiler rooms, electrical panels, and mechanical areas are clean, accessible, and code-compliant
- Maintain proper function of door hardware, locks, keypads, and access controls
- Assist in ensuring compliance with fire safety and life-safety regulations
- Support classroom, office, and program moves as needed
- Assist with setting up and breakdown of special events, including after-hours and overnight needs
- Provide regular updates to the VP of Facilities +Real Estate regarding staff performance, equipment needs, site conditions, and urgent issues
- Support emergency response efforts and remain available for after-hours calls as required
- Assist with opening and supporting new locations and providing coverage for staffing needs
- Perform routine security checks of all entry points
- Monitor and maintain appropriate temperatures throughout buildings
- Ensure all lighting, HVAC, and systems are properly powered on/off daily
- Report suspicious activity or safety concerns to management
- Perform maintenance tasks including bulb & fixture replacement, plumbing, carpentry, painting, and other repairs as needed.
- Emergency response including after-hours contact
Qualifications
- High School Diploma or equivalent
- Valid driver’s license and reliable transportation required
- Proficient in the proper use of hand and power tools
- Working knowledge of basic carpentry, plumbing, electrical, HVAC, and general building maintenance
- Prior supervisory or lead maintenance experience preferred
- Ability to work independently and manage multiple priorities across sites
- Microsoft Office suite of products
Working Conditions
- Ability to work across all assigned locations year-round
- Availability for after-hours and emergency response as needed
Physical Requirements
- Ability to lift heavy objects
- Ability to work indoors and outdoors in all seasons and weather conditions
- Ability to remain standing for extended periods of time
Benefits for Full Time Employees
Gersh Autism offers a competitive compensation package, including comprehensive health and retirement benefits, professional development opportunities, and a supportive work environment committed to your personal and professional growth.
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k matching)
- Life Insurance (Basic, Voluntary & AD&D)
- Family Leave (Maternity, Paternity)
- Short-Term & Long-Term Disability
- Training & Development
The pay range for this role is:
70,000 - 80,000 USD per year(West Hempstead)
Salary Context
This $70K-$80K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 1414 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 Gersh Autism, 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 $210,000 based on 1,345 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $202,000. This role's midpoint ($75K) sits 64% below the category median. Disclosed range: $70K to $80K.
Across all AI roles, the market median is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. For comparison, the highest-paying categories include Research Scientist ($260,000) and AI Architect ($251,680). By seniority level: Entry: $125,000; Mid: $202,000; Senior: $240,000; Director: $255,600; VP: $225,000.
Gersh Autism AI Hiring
Gersh Autism has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in West Hempstead, NY, US. Compensation range: $80K - $80K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: New York (228 roles, $223,400 median); San Francisco (216 roles, $255,750 median); Los Angeles (172 roles, $204,300 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 $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. Highest-paying categories: Research Scientist ($260,000 median, 48 roles); AI Architect ($251,680 median, 9 roles); Research Engineer ($250,200 median, 8 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 $220,000. Top-quartile roles start at $260,000, and the 90th percentile reaches $311,800. 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. Research Scientist roles lead at $260,000 median, while AI/ML Engineer roles sit at $210,000. 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: 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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