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About This Role
NYU Grossman School of Medicine is one of the nation's top\-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care. At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.
*For more information, go to* *med.nyu.edu*, and interact with us on *LinkedIn*, *Glassdoor*, *Indeed*, *Facebook*, *X* *and* *Instagram*.
Position Summary:
We have an exciting opportunity to join our team as a Program Manager, AI.
Reporting to the Director of Operations for the Technology Opportunities and Ventures (TOV) Office, the Program Manager for AI \&Compliance will lead the business\-side strategy, commercialization, training, and compliance oversight of AI powered solutions. This role is embedded within the business and partners closely with technology teams, legal, finance, and other stakeholders to ensure AI solutions are practical, compliant, and aligned with TOVs commercial objectives. It spans four core verticals:
Training \& Adoption: Develop and deliver training programs for staff and partner business groups to help them understand and use AI powered agents effectively; measure adoption and skills uptake. Support business groups in identifying appropriate use cases and using approved agent\-development tools within established institutional guidelines.
Commercialization: Shape go to market strategies and manage licensing and partnerships with a business lens, while working with technology teams to translate product capabilities into customer value. Translate agent capabilities into clear customer value, develop business cases and market assessments, and coordinate with legal, finance, MCIT, and other technology stakeholders to ensure commercialization plans accurately reflect the products technical capabilities, security requirements, support model, and production\-readiness constraints.
AI Agent Stewardship: Coordinate cross functional teams (business process owners, user experience leads, and technical colleagues) to design, configure, and deploy AI agents that streamline administrative workflows. Translate business requirements into functional agent workflows, prompts, knowledge sources, decision logic, validation steps, and structured outputs. Develop working prototypes and business solutions using institutionally approved platforms, while partnering with MCIT on architecture, cybersecurity, integrations, scalability, production deployment, and long\-term technical support. This role is expected to have the hands\-on capability to build AI agents and is not limited to coordinating the work of technical teams or vendors.
Compliance: Oversee adherence to intellectual property policies, data privacy requirements, and institutional guidelines; serve as the business liaison to legal and compliance teams. Serve as the business liaison to MCIT, Legal, Compliance, Finance, and other oversight functions. Maintain appropriate documentation, testing records, approvals, ownership models, and change controls, and ensure solutions requiring enterprise integration, elevated access, production deployment, or ongoing technical support proceed through the appropriate MCIT review and governance processes.
Job Responsibilities:
- Training \& Change Management
- Design and deliver training curricula (e.g., workshops, user guides, webinars) for TOV staff and other business units, ensuring content is accessible to non technical audiences.
- Establish a continuous feedback loop to refine training materials based on learner feedback and new product features.
- Develop a readiness plan for other business groups to tailor and adopt AI agents with minimal configuration; measure training effectiveness by tracking post training adoption and user satisfaction
2\. Commercialization \& Partnerships
- Partner with technology and finance teams to develop business cases, market assessments, and go to market strategies for AI enabled products.
- Lead outreach to potential licensees and partners, draft term sheets or amendments in coordination with legal, and manage commercialization pipelines. Use AI tools to monitor commercialization milestones, generate executive level updates, and report on revenue and pipeline metrics
3\. AI Agent Development \& Program Management
- Design, build, configure, test, and continuously improve AI agents using institutionally approved platforms.
- Translate business requirements into functional workflows, prompts, knowledge sources, decision logic, validation steps, and structured outputs.
- Develop working prototypes, conduct user testing, troubleshoot performance issues, and refine agents based on feedback and results.
- Partner with MCIT on architecture, security, integrations, production deployment, and long\-term support, ensuring solutions follow institutional technology\-governance requirements.
- Coordinate the business side planning of AI agent development projects from requirements gathering through testing and production. Assemble cross functional teams (including business process owners, experience leads, and technical colleagues), assign tasks, monitor progress, and manage vendor relationships.
- Define and track key performance indicators (KPIs) for agent effectiveness, user adoption, user satisfaction, and operational efficiency. Maintain a dashboard that reports monthly usage statistics and target outcomes (e.g., 90% utilization) for TOV leadership and use data to inform improvement priorities.
- Manage a regular cadence for enhancements: facilitate user feedback sessions, coordinate update reviews with relevant stakeholders (business leads, compliance, and technical teams), and ensure updates are documented and deployed at scheduled interval
4\. Compliance \& Governance
- Serve as the primary liaison for AI program compliance across Finance, ensuring all activities follow NYU patent policies, data privacy rules, and other institutional requirements.
- Support monitoring of licensee diligence and contractual obligations; maintain accurate records in the Sophia database.
- Facilitate regular status meetings, prepare and present program updates, and maintain comprehensive documentation of program activities.
5\. Collaboration \& Stakeholder Management
- Maintain open dialogue with the Delivery Leada role focused on overall project execution, timelines, and stakeholder coordinationlinkedin.comto align product delivery timelines and mitigate risks.
- Lead analytics discussions to measure experience success metrics with experience leads, operational/business leads, product managers, and technology teams; identify improvement opportunities grounded in quantitative data
6\. Additional responsibilities as assigned.
Minimum Qualifications:
To qualify you must have a
- Demonstrated hands\-on experience designing, building, configuring, testing, and improving AI agents, workflow automations, or other AI\-enabled business solutions.
- Ability to translate business and operational requirements into functional AI workflows, prompts, knowledge sources, decision logic, validation rules, and structured outputs.
- Experience developing prototypes and business solutions using generative AI, low\-code, no\-code, or workflow\-development platforms.
- Demonstrated ability to work effectively within enterprise technology\-governance structures and partner with technical teams on security, architecture, integrations, and production deployment.Requires Bachelor's degree or an equivalent combination of education and experience.
- Familiarity with intellectual property protection and contracts.
- Excellent written and verbal communication skills
- A strong relationship builder and a good team player.
- Detailed and highly organized with initiative, drive and superior time management skills.
- Ability to work independently, set priorities, manage multiple tasks, and meet deadlines.
- Demonstrated commitment to outstanding customer service.
- Considerable experience organizing and processing complex information
- Strong project management skills.
- Excellent customer service skills.
Preferred Qualifications:
- 5\+ years of experience in AI program management, commercialization, or compliance.
- Ability to review and interpret various types of contracts and agreement terms.
- Familiarity with cloud\-based platforms (e.g., Azure AI, Databricks).
- PMP preferred.
*Qualified candidates must be able to effectively communicate with all levels of the organization.*
NYU Grossman School of Medicine provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you'll feel good about devoting your time and your talents. At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package. Our offerings provide a robust support system for any stage of life, whether it's developing your career, starting a family, or saving for retirement. The support employees receive goes beyond a standard benefit offering, where employees have access to financial security benefits, a generous time\-off program and employee resources groups for peer support. Additionally, all employees have access to our holistic employee wellness program, which focuses on seven key areas of well\-being: physical, mental, nutritional, sleep, social, financial, and preventive care. The benefits and wellness package is designed to allow you to focus on what truly matters. Join us and experience the extensive resources and services designed to enhance your overall quality of life for you and your family.
NYU Grossman School of Medicine is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration. We require applications to be completed online.
View Know Your Rights: Workplace discrimination is illegal.
NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $84,577\.93 \- $100,978\.53 Annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.
To view the Pay Transparency Notice, please click here
Salary Context
This $84K-$100K range is in the lower quartile 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 NYU Langone Health, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($92K) sits 57% below the category median. Disclosed range: $84K to $100K.
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.
NYU Langone Health AI Hiring
NYU Langone Health has 3 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Based in New York, NY, US. Compensation range: $90K - $126K.
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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