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About This Role
About NYC Health \+ Hospitals
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NYC Health \+ Hospitals is the largest public health care system in the United States. We provide essential outpatient, inpatient and home\-based services to more than one million New Yorkers every year across the city’s five boroughs. Our large health system consists of ambulatory centers, acute care centers, post\-acute care/long\-term care, rehabilitation programs, Home Care, and Correctional Health Services. Our diverse workforce is uniquely focused on empowering New Yorkers.
At NYC Health \+ Hospitals, our mission is to deliver high quality care health services, without exception. Every employee takes a person\-centered approach that exemplifies the ICARE values (Integrity, Compassion, Accountability, Respect, and Excellence) through empathic communication and partnerships between all persons.
Work Shifts
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9:00 A.M – 5:00 P.M
Duties \& Responsibilities
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Purpose of Functional Assignment:
Under direction of the Vice President/Chief Data and Artificial Intelligence Officer, implements Artificial Intelligence (AI)
to enhance high\-quality care, optimize workflows, and ensure equity in healthcare delivery.Manages engineering
teams, sets technical direction, and works collaboratively with cross\-functional partners to translate AI research into
real\-world, production\-grade AI solutions that deliver measurable improvements in patient care, financial and/or
operational efficiency.
Essential Duties and Responsibilities:
1\. Leads AI engineers, data scientists, and Machine Learning professionals, ensuring technical excellence and
consistent delivery of scalable AI solutions.
2\. Defines and executes AI engineering roadmaps in partnership with senior leadership and aligns them with
organizational goals.
3\. Oversees the full lifecycle of AI/ML systems — from design and prototyping to deployment, monitoring, and related
work.
4\. Guides architecture decisions for AI platforms, data pipelines and model serving infrastructure; evaluates and
integrates new AI tools and frameworks as needed.
5\. Establishes and maintains best practices for model development, code quality, version control, model lifecycle
management, and Machine Learning.
6\. Develops and maintains collaborations with academic institutions, research organizations, and industry partners to
advance AI capabilities.
7\. May contribute to peer\-reviewed publications presents at conferences, and stays engaged with the AI research
community to keep the System at the forefront of innovation.
8\. Collaborates with product, data, and infrastructure teams to align AI capabilities with enterprise needs; acts as a
trusted technical advisor to the System’s stakeholders and non\-technical partners.
Minimum Qualifications
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1\.Master's degree from an accredited college or university in Computer Science, Engineering, or related discipline; and eight (8\) years of experience in software/AI engineering, four (4\) years of which must have been in a leadership role managing cross\-functional technical teams such as AI/ML engineers, data scientists, and Machine Learning professionals, and with a proven track records of delivering enterprise\-grade AI solutions; or
2\. A satisfactorily equivalent combination of education, training, and experience. However, all candidates must have a minimum of a Bachelor’s Degree in disciplines listed in “1” above, or in a related discipline, and preference will be given to applicants with a doctorate degree.
Preferred Certifications:
1\. Google Cloud Professional Machine Learning Engineer.
2\. AWS Certified Machine Learning – Specialty.
3\. Microsoft Certified: Azure AI Engineer Associate.
4\. Certified Kubernetes Administrator.
5\. TensorFlow Developer Certificate.
6\. HL7 FHIR Proficiency Certification.
7\. Databricks Certified Professional Data Engineer.
Preferred Knowledge Areas, Skills, Abilities, and other Qualifications:
1\. Proven expertise in machine learning, deep learning, generative AI, and AI system design and deployment.
2\. Proficient in Python, TensorFlow/PyTorch, cloud services (AWS, Azure, GCP), and MLOps tools.
3\. Strong knowledge of Machine Learning practices, including model versioning, CI/CD for Machine Learning, and production monitoring.
4\. Experience deploying large\-scale AI systems in production environments.
5\. Programming Languages \& Frameworks: Python/R, TensorFlow, PyTorch, Keras, Scikit\-learn, XGBoost, LightGBM.
6\. Data Engineering \& Big Data: SQL and NoSQL databases (e.g., PostgreSQL, MongoDB), Apache Spark, Databricks, Airflow.
7\. Cloud Platforms: AWS (e.g., SageMaker, EC2, S3\), Microsoft Azure (e.g., Azure ML, Databricks), Google Cloud Platform (e.g., Vertex AI, BigQuery).
8\. Machine Learning \& Deployment: Docker, Kubernetes, MLflow, Kubeflow, CI/CD tools (GitHub Actions, Jenkins, GitLab CI).
9\. Version Control \& Collaboration: Git, GitHub, GitLab, JIRA, Confluence.
10\. Visualization \& BI Tools: Tableau, Power BI, Looker, Jupyter Notebooks, VS Code, PyCharm.
11\. APIs \& Integration: FastAPI, Flask, and tools for secure model deployment and EHR system integration (where applicable).
12\. Proven track record of academic engagement, including collaborations, publications, or conference presentations in AI/ML fields.
Equipment/Machines and Software Operated:
1\.General office equipment (e.g., computer, phones, scanner, copier)
Benefits
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NYC Health and Hospitals offers a competitive benefits package that includes:
- Comprehensive Health Benefits for employees hired to work 20\+ hrs. per week
- Retirement Savings and Pension Plans
- Paid Holidays and Vacation in accordance with employees' Collectively bargained contracts
- Loan Forgiveness Programs for eligible employees
- College tuition discounts and professional development opportunities
- College Savings Program
- Union Benefits for eligible titles
- Multiple employee discounts programs
- Commuter Benefits Programs
How To Apply
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If you wish to apply for this position, please apply online by clicking the "Apply for Job" button.
*Note: Candidates selected for a position are required to come to NYC as part of their onboarding.*
Salary Context
This $225K-$265K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At NYC Health + Hospitals, 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($245K) sits 37% above the category median. Disclosed range: $225K to $265K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
NYC Health + Hospitals AI Hiring
NYC Health + Hospitals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $265K - $265K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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