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About This Role
FLSA: Exempt/Overtime Ineligible
Benefits: Eligible
Hours Per Week: 40/Full\-time
*Met Council is America’s largest Jewish charity dedicated to serving the needy. We fight poverty through comprehensive social services and by treating each client with compassion, integrity, and respect. Our programs are staffed by experts who help over 325,000 clients each year and advocate on behalf of all needy New Yorkers. Our services include 100% affordable housing at 21 locations, family violence services, Holocaust survivor assistance, geriatric social work, crisis intervention and the largest free kosher food distribution program in the world. Our network of 101 food pantries, affordable housing sites, and affiliated JCCs provide services directly in neighborhoods across New York City.*
Position Summary:
Met Council seeks a creative, entrepreneurial, and experienced technical leader to serve as its first Senior Director of Data \& AI Systems. This is a hands\-on technical leadership role. This individual will own the long\-term vision for—and the ongoing build of—Met Council’s data architecture, information systems, and responsible AI adoption, unifying data across the organization’s core platforms, strengthening data governance, and advancing the systems that make trustworthy reporting and AI possible.
The successful candidate will build sustainable, scalable data and technology solutions in partnership with internal teams and external vendors, and design accessible, user\-friendly systems in collaboration with diverse users across the organization. The Senior Director of Data \& AI Systems will lead an in\-house technical team, partner with internal and external stakeholders, including government agencies, community organizations, and financial institutions, to ensure the usability, design, functionality, and sustainability of Met Council’s platforms for its extensive network of clients and partners across New York City.
Principal Responsibilities:
*Data Infrastructure \& Integration*
- Serving as product owner across Met Council’s departmental Salesforce instances, ensuring both day\-to\-day functionality and continuous improvement within a well\-governed data environment.
- Own the evolution of Met Council’s data systems and architecture to meet programmatic needs while maintaining the highest levels of data integrity.
- Maintain and strengthen the data foundation, including pipelines, integration middleware, and warehousing, that supports organization\-wide business intelligence and reporting.
- Overseeing the data operations and analytics function, and providing strategic direction on related projects.
- Supporting the integration of data visualization and business intelligence tools, such as Tableau, within and across the organization.
- Define project metrics and return\-on\-investment goals for data and technology initiatives, track performance post\-delivery, and communicate results regularly with stakeholders.
*AI \& Data Governance*
- Recommend, own, and implement AI and data\-governance policy across client\-data systems, including PII protection, vendor Business Associate Agreement (BAA) compliance, and the secure and responsible use of artificial intelligence.
- Recommend, oversee, and implement technology solutions and policies that align with Met Council’s holistic approach to client service and its mission to move individuals from crisis to stability.
- Support AI tools and vendor engagements through secure, compliant deployment.
*Collaboration \& Partnerships*
- Coordinate technology initiatives with external vendors, consultants, software providers, and security advisors, evaluating and recommending solutions in partnership with Met Council teams.
- Supporting Met Council’s departments and its growing data\-analysis needs, fostering collaboration and attracting technical capability.
- Fostering interdepartmental collaboration through technology by integrating systems that facilitate communication and data sharing.
*Technology Leadership*
- Leading technological capacity building initiatives, including selecting appropriate technologies, negotiating licensing, and implementing them effectively.
- Optimizing resource allocation through strategic technology investments and fostering collaborations with technology partners
- Driving digital transformation efforts within the organization, identifying and implementing advanced technologies to improve service delivery and boost operational efficiency.
- Promoting inclusivity and accessibility by overseeing the development and maintenance of digital resources that are usable by individuals with disabilities and diverse cultural backgrounds.
- Assembling and managing a team of admins and analysts.
Skills and Education:
- 5\+ years of technological product and/or project\-management experience (required); 7\+ years in data, technology, or systems leadership preferred
- Data architecture and systems integration experience—demonstrated ownership of data platform integration, ideally across Salesforce and/or a modern data warehouse with integration middleware
- Data governance and security experience (strongly preferred) and familiarity with PII protection, BAAs, and handling sensitive client data
- Bachelor’s degree (required); relevant advanced degree or technical certifications a plus
- Continuous Learning: A commitment to staying current with technological advancements through workshops, conferences, online courses, and relevant certifications
Compensation: $135,000 to $155,000 per year.
Benefits: Major medical, dental, vision, and life insurance; pre\-tax commuter benefits; FSA; 403(b) with employer contribution; plus generous vacation, sick leave, and holidays.
Salary Context
This $135K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Metropolitan Council on Jewish Poverty, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($145K) sits 33% below the category median. Disclosed range: $135K to $155K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Metropolitan Council on Jewish Poverty AI Hiring
Metropolitan Council on Jewish Poverty has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $155K - $155K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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