Interested in this AI/ML Engineer role at Children's Specialized ABA?
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About Children’s Specialized ABA
Children’s Specialized ABA is designed to address the comprehensive needs of children diagnosed with Autism Spectrum Disorder (ASD). By leveraging the expertise of the Children’s Specialized Hospital Autism Center of Excellence, the program aims to expand access to innovative and compassionate care, empowering children diagnosed with autism to thrive. Children’s Specialized ABA offers home\-based, community\-based, and center\-based ABA therapy.
At Children’s Specialized ABA, we envision a future where every child diagnosed with autism has access to innovative and compassionate care, empowering them to thrive and reach their full potential. Our vision is built on four core values:
- Inclusivity: We celebrate the diversity within the Autism spectrum and are committed to creating an inclusive environment that respects and values each person’s individual strengths and differences.
- Innovation: We foster a culture of creativity and collaboration, exploring new ideas to develop personalized solutions that enhance quality of life for all children with Autism.
- Connection: We actively engage with the health systems and broader community to coordinate services and care for people with Autism.
- Quality and Safety: We invest in research and training to provide cutting\-edge, effective, safe, and personalized services tailored to the unique needs of those we serve.
Join Us as a the Director of Technology, Automation and AI Solutions!
The Director of Technology, Automation \& AI Solutions will serve as a strategic operator and solution architect responsible for designing and executing a comprehensive technology and automation roadmap across the organization. This individual will assess business needs end\-to\-end and determine where to build custom solutions, leverage AI/agentic tools, optimize existing systems, or procure external platforms.
This role requires a hands\-on, systems\-thinking leader who can translate operational challenges into scalable, cost\-effective solutions—owning everything from concept through implementation, vendor selection, and ROI modeling. While overseeing IT security and compliance is part of the role, the primary focus is on innovation, automation, and building an integrated, future\-ready technology ecosystem.
What You'll Do:
AI, Automation \& Solutions Strategy
- Own and define a multi\-year technology and AI roadmap, aligning business priorities with scalable system and automation solutions
- Evaluate organizational workflows and identify opportunities to:
- Build custom/internal tools
- Deploy automation and agentic AI solutions
- Optimize or replace existing systems
- Source and implement external platforms
- Create clear decision frameworks for build vs. buy vs. optimize, including cost\-benefit analyses and ROI projections
- Design and deploy agentic and AI\-enabled solutions that automate workflows and enhance decision\-making across departments
- Stay at the forefront of emerging technologies and AI capabilities, rapidly testing and scaling high\-impact use cases
Technology Portfolio \& Investment Management
- Own the full technology portfolio across departments, including SaaS tools, automation platforms, and internally built solutions
- Analyze and rationalize current tech stack to identify redundancies, gaps, and opportunities for consolidation or enhancement
- Develop and manage budgets across all technology investments, including:
- Vendor costs
- Internal build vs. outsource decisions
- Ongoing maintenance and scalability costs
- Lead vendor selection, contract negotiation, and performance management
- Build detailed financial models to inform leadership decisions on major technology investments
Solutions Architecture, Implementation \& Operations
- Act as the organization’s solutions architect, connecting systems, data, and workflows into a cohesive ecosystem
- Lead end\-to\-end solution design—from identifying problems through implementation and adoption
- Partner cross\-functionally to map workflows, identify inefficiencies, and implement scalable solutions
- Ensure successful rollout of all systems, including change management, adoption, and measurable impact
- Establish KPIs and dashboards to track performance, utilization, and ROI of all technology investments
Systems Integration, Scalability \& M\&A
- Lead enterprise\-wide systems integration and ensure interoperability across platforms
- In M\&A scenarios:
o Assess technology stacks of acquisition targets
o Define integration or replacement strategies
o Establish scalable infrastructure to support growth
o Design systems that support long\-term scalability, operational efficiency, and data visibility
What You'll Need
- A Bachelors Degree or equivalent experience
- 7\+ years of experience in solutions architecture, technology strategy, automation, or operations\-focused technology roles
- Demonstrated experience building or implementing end\-to\-end solutions across business functions
- Strong expertise in:
- AI, automation, and/or agentic solutions
- Build vs. buy decision\-making and technology evaluation
- Systems integration and architecture
- + Financial modeling and cost analysis of technology investments
- Experience owning technology roadmaps and portfolios, not just executing projects
- Proven ability to translate business needs into scalable, practical, and cost\-effective solutions
- Experience working cross\-functionally to drive adoption and operational change
- Exposure to IT security and compliance best practices (not required as primary specialization)
Work Location \& Hours:
This is a hybrid position, with the primary work location in New Jersey. Our locations include Kearny, Newark, Voorhees, Princeton, Clark, Clifford, Jackson. The core work hours are 8:30am to 5:00pm.
Why Work With Children’s Specialized ABA?
We’re an amazing ABA provider! We take a *whole\-child*, *whole\-caregiver* approach. Our integrated model combines ABA therapy with speech, occupational therapy, and behavioral health support. You’ll be part of a deeply collaborative, mission\-driven team.
Here’s what you can expect:
- Up to 29paid days off in your first year (including PTO, sick time, and holidays); earned on an accrual basis, paid time off increases with tenure
- Comprehensive benefits including FREE medical (for employee, buy\-up for dependent/partner coverage), voluntary dental, vision, short\-term disability, critical illness coverage, and more!
- Free 50k life insurance policy.
- Free Employee Assistance Program (EAP).
- 401(k) retirement savings plan
- Company discount program – discounts of amusement parks, memberships, cruises, movie tickets, spas, sports ticks and more.
Reasonable Pay Estimate
A reasonable estimate of the pay range for this position is $170,00\.00 to $210,000\.00 per year, with additional performance based incentive compensation. There are numerous factors taken into consideration in determining the actual offered rate of pay, including but not limited to: job\-related qualifications, experience, skills, education, geographic location, and consideration of internal and experience equity.
*Children’s Specialized ABA provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.*
Salary Context
This $170K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Children's Specialized ABA, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($190K) sits 5% above the category median. Disclosed range: $170K to $210K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Children's Specialized ABA AI Hiring
Children's Specialized ABA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Newark, NJ, US. Compensation range: $210K - $210K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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