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About This Role
Job Summary:
92NY seeks an accomplished and visionary technology executive to serve as its Senior Vice President, AI, Technology and Enterprise Transformation. Serving as a key member of 92NY’s Senior Leadership Group, the Vice President will develop and execute a comprehensive technology strategy that advances the organization's mission, strengthens operational excellence, and enhances the experience of its patrons, members, donors, students, artists, and staff.
The Senior Vice President will lead a team of professionals who, collectively, are responsible for the organization's technology functions, including IT Operations, Web \& Digital Platforms, and Enterprise Applications \& Business Systems, while serving as the strategic advisor to, and the individual with implementation responsibilities on behalf of, executive leadership in connection with technology, cybersecurity, AI and digital innovation, data strategy, and emerging technologies.
This executive will modernize and integrate the organization's technology ecosystem, creating a connected enterprise that supports a unified view of constituents across programs, membership, fundraising, ticketing, marketing, finance, and customer service. The Senior Vice President will champion digital transformation initiatives, including AI initiative, that improve organizational effectiveness, strengthen decision\-making through data, and deliver exceptional digital experiences.
The successful candidate will be a collaborative and forward\-thinking leader and doer who combines strategic vision with operational excellence, builds high\-performing teams, is comfortable rolling up his or her sleeves and getting involved in implementation and translates technology investments into measurable organizational outcomes.
Job Responsibilities Include:
The ideal candidate would have strength and experience in the following areas, with a particular emphasis on Strategic Leadership \& Digital Architecture and Transformation and Enterprise Applications, Data and Business Systems. *Strategic Leadership \& Digital Architecture and Transformation*
Provide strategic leadership for the organization's technology vision and digital transformation agenda.* Develop and execute a multi\-year technology and digital transformation strategy aligned with organizational priorities and long\-term business objectives.
- Serve as a trusted advisor to executive leadership on technology strategy (including AI strategy), innovation, risk, and emerging technologies.
- Lead enterprise\-wide transformation initiatives that modernize business processes, improve operational efficiency, and enhance constituent experiences.
- Develop strategies for responsible adoption of artificial intelligence, along with other emerging technologies that improve productivity and organizational effectiveness.
- Lead the organization's cloud strategy to improve scalability, resilience, performance, and cost efficiency.
- Develop technology investment priorities and annual capital and operating budgets.
- Establish governance structures that ensure technology initiatives align with organizational strategy and deliver measurable business value.
*Enterprise Applications, Data and Business Systems*
Lead the strategy, governance, and optimization of the organization's enterprise technology platforms.* Oversee various platforms, including Salesforce, and the organization's enterprise applications portfolio, with the goal of consolidating and better integrating platforms and ensuring that they can seamlessly communicate with one another.
- Develop an integrated technology ecosystem connecting CRM, fundraising, ticketing, registration, finance, marketing, customer service, and other business systems.
- Establish enterprise data governance standards that improve data quality, reporting, and organizational decision\-making.
- Expand automation and workflow optimization to improve efficiency across departments.
- Create a unified, 360\-degree constituent view supporting engagement, fundraising, programming, and analytics.
- Maintains a hands\-on approach to major technology initiatives, remaining actively engaged in solution design, implementation, and organizational adoption.
*Technology Infrastructure, Cloud and Cybersecurity*
Provide executive oversight for secure, resilient, and scalable technology operations.* Lead IT Operations, infrastructure, networking, cloud services, end\-user computing, and technology support.
- Develop and maintain a comprehensive cybersecurity strategy, including risk management, incident response, disaster recovery, business continuity, and security awareness.
- Ensure technology infrastructure meets evolving organizational needs while maintaining high levels of availability, performance, and security.
- Partner with external security, infrastructure, and managed service providers to maintain best practices.
- Establish technology policies and governance that support compliance, privacy, and responsible risk management.
*Digital Experience and Innovation*
Lead the evolution of 92NY's digital platforms to enhance constituent engagement and organizational impact.* Oversee website technology, digital platforms, search capabilities, analytics, and customer\-facing technologies.
- Partner with business leaders to improve digital experiences that increase engagement, participation, fundraising, and customer satisfaction.
- Utilize analytics and user insights to continuously improve digital performance.
- Identify opportunities to leverage technology to strengthen programming, communications, and constituent engagement.
*Organizational Leadership and Talent Development*
Build and lead a high\-performing technology organization.* Provide strategic leadership for IT Operations, Web \& Digital Platforms, and Enterprise Applications \& Business Systems.
- Recruit, develop, mentor, and retain exceptional technology talent.
- Foster a culture of collaboration, accountability, innovation, customer service, and continuous improvement.
- Develop leadership capabilities throughout the technology organization.
- Promote strong partnerships across departments and cultivate technology literacy throughout the organization.
*Vendor, Portfolio and Financial Management*
Ensure technology investments deliver organizational value.* Oversee relationships with technology vendors, consultants, implementation partners, and managed service providers.
- Negotiate contracts, licensing agreements, statements of work, and service\-level agreements.
- Establish project portfolio management practices that prioritize initiatives based on strategic value and organizational capacity.
- Ensure projects are delivered on schedule, within budget, and achieve intended business outcomes.
- Develop technology investment strategies that maximize organizational return while effectively managing risk.
Experience, Education, \& Skills Desired:
- Bachelor's degree in Information Technology, Computer Science, Business Administration, or a related field; advanced degree and relevant certifications preferred.
- 10\+ of progressive technology leadership experience with increasing executive responsibility.
- Demonstrated success leading enterprise technology strategy, digital transformation, cybersecurity, cloud modernization, and organizational change.
- Significant experience with enterprise applications, CRM platforms (preferably Salesforce), systems integration, data governance, analytics, and digital platforms.
- Expertise relating to AI.
- Experience leading multidisciplinary technology teams and managing complex technology portfolios.
- Strong financial, vendor, and project portfolio management experience.
- Successful track\-record of creating strategic and transformational technology plans while balancing budgetary investment constraints.
- Exceptional communication skills with the ability to translate technical concepts into strategic business decisions.
- Experience in nonprofit, cultural, educational, membership\-based, or mission\-driven organizations is preferred.
Work Environment \& Requirements:
40 hours/week
Application Instructions
Please forward a CV and cover letter with salary requirements.
Due to a high volume of applications that we receive, we are only able to contact those applicants whose experience most aligns with the position profile.
Compensation Range
$250,000 \- $275,000 *The actual compensation offered will be based on a number of factors including, but not limited to the qualifications of the applicant, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location in which the applicant lives and/or from which they will be performing the job.*
Salary Context
This $250K-$275K range is above the 75th percentile 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 92nd Street Y, 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. This role's midpoint ($262K) sits 22% above the category median. Disclosed range: $250K to $275K.
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.
92nd Street Y AI Hiring
92nd Street Y has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $275K - $275K.
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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