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About This Role
Position Summary
IRI is seeking an organized, operationally focused, and forward\-thinking Innovation and AI Manager to support the organization’s growing AI and operational innovation initiatives. This role will help standardize, coordinate, and support the responsible use of AI across departments including Human Resources, Finance, Compliance, Quality Assurance, Program Operations, and Administrative Services.
The Innovation and AI Manager will work directly with executive leadership and serve as the operational lead for day\-to\-day AI coordination, implementation support, training, documentation, governance tracking, and workflow standardization. This is not a highly technical engineering role. Instead, the position is focused on practical implementation, project coordination, staff support, and operational integration of AI tools into business processes.
This position is ideal for a candidate with strong project management, organizational, training, and operational skills who is interested in emerging technology and process improvement within a mission\-driven nonprofit environment.
Reporting Structure
Reports to: CFO/Chief Human Resources Officer (Executive Sponsor for AI Initiatives)
Works closely with:
- Executive Leadership
- HR
- Finance
- QA \& Compliance
- Residential Operations
- Administrative Services
- AI Committee
Core Responsibilities
AI Program Coordination
- Coordinate organization\-wide AI initiatives and projects
- Help implement and maintain standardized AI practices across departments
- Track AI\-related projects, pilots, and operational initiatives
- Maintain implementation timelines, status updates, and follow\-up items
- Assist leadership with rollout planning and project execution
- Workflow \& Operational Support
- Help departments identify repetitive or administrative processes that can be improved using AI
- Assist in creating standardized AI workflows, templates, and prompt libraries
- Support development of department\-specific AI tools and resources
- Organize and maintain AI documentation, guides, and knowledge resources
- Training \& Staff Support
- Coordinate AI trainings and educational sessions
- Create user\-friendly guides, SOPs, and training materials
- Provide day\-to\-day support to staff using approved AI tools
- Help reinforce organizational AI policies and best practices
- Support onboarding of staff into approved AI platforms
- Governance \& Compliance Support
- Assist with monitoring compliance with organizational AI policies
- Help maintain AI usage documentation and governance records
- Track approved and restricted use cases
- Support leadership in maintaining responsible and secure AI practices
- Research \& Continuous Improvement
- Stay informed on practical AI tools and operational use cases
- Research low\-risk AI applications relevant to nonprofit and healthcare environments
- Assist leadership in evaluating tools and identifying process improvement opportunities
Qualifications
Education\-Bachelor’s degree preferred in one of the following areas:
- Business Administration
- Operations Management
- Organizational Leadership
- Information Systems
- Healthcare Administration
- Human Resources
- Project Management
- Related field
Equivalent experience may be considered.
Experience
Preferred Qualifications
- 2–5 years of project coordination, operations, administrative management, or process improvement experience
- Strong organizational and documentation skills
- Experience coordinating cross\-functional projects
- Excellent written and verbal communication skills
- Strong Microsoft Office and/or Google Workspace skills
- Comfortable learning and using AI platforms such as:
- ChatGPT
- Microsoft Copilot
- Google Gemini
- Other productivity\-based AI tools
- Strongly Preferred
- Experience in nonprofit, healthcare, OPWDD, compliance, or human services environments
- Experience creating training materials or conducting staff trainings
- Experience with workflow/process improvement initiatives
- Interest in operational innovation and emerging technologies
Ideal Candidate Profile
The ideal candidate is:
- highly organized,
- proactive,
- detail\-oriented,
- operationally minded,
- comfortable coordinating multiple projects,
- and excited about practical AI implementation.
This person does not need to be a programmer or software engineer but should be comfortable learning technology quickly and helping others adopt new tools responsibly.
Pay: $65,000\.00 \- $80,000\.00 per year
Benefits:
- Dental insurance
- Employee assistance program
- Employee discount
- Flexible spending account
- Health insurance
- Life insurance
- Paid time off
- Parental leave
- Tuition reimbursement
- Vision insurance
Education:
- Bachelor's (Required)
Experience:
- Project coordination: 2 years (Preferred)
- AI Tools: 2 years (Preferred)
Work Location: In person
Salary Context
This $65K-$80K range is in the lower quartile 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 IRI, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($72K) sits 60% below the category median. Disclosed range: $65K to $80K.
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.
IRI AI Hiring
IRI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Elmont, NY, US. Compensation range: $80K - $80K.
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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