Interested in this AI/ML Engineer role at American Nurses Association, Inc?
Apply Now →About This Role
The Program Manager, A.I., Nursing and Digital Health plans is key role for this grant funded position, and will direct and coordinate activities associated with programs at the intersection of nursing practice and Artificial Intelligence (AI). As Program Manager, you will develop and manage assigned client and stakeholder relationships in a manner consistent with organizational policies and procedures; schedules and facilitates program\-support meetings; and ensures the timely completion and distribution of required documentation.
Reporting to the Vice President of AI and Digital Health Programs , you will monitor contractual and grant obligations to support compliance with applicable requirements. You will prepare and assist with the review of monthly status reports, test plans, design data books, and process material specifications as required. As Program Manager, you will also plan, lead and coordinate meetings related to project timing, goals, and budgets to support internal and customer expectations and compliance with organizational and contractual requirements. You will also provide key financial data to the Vice President of AI and Digital Health Programs on a timely basis; create, maintain, and update program schedules for contract deliverables and key events; and assists in developing and presenting presentations that communicate program strategies, products, outcomes, and results.
In addition, you will provide subject\-matter support related to AI in nursing, and contribute to the development, implementation, and evaluation of AI\-enabled initiatives that support nursing practice, workforce development, and healthcare delivery.
WHAT YOU WILL DO:
- Serve as Nurse Planner for assigned continuing nursing education activities, ensuring that activities are planned, implemented, and evaluated in accordance with applicable accreditation criteria and organizational standards.
- Collaborate with subject\-matter experts, faculty, planners, and internal stakeholders to identify educational needs, define measurable learning outcomes, and support evidence\-based content development.
- Review educational activity materials for alignment with nursing professional\-development standards, learner needs, conflict\-of\-interest requirements, and continuing education documentation requirements.
- Support disclosure, mitigation, and documentation processes related to relevant financial relationships, bias, commercial support, and accreditation compliance requirements.
- Collaborate closely with the grant implementation team with limited supervision, and manage a broad range of departmental project work involving volunteer boards, coalitions, consultants, and partners.
- Coordinate evaluation tools, learner feedback, attendance records, certificates, and activity files to support complete and accurate continuing education records.
- Monitor activity outcomes and evaluation data to identify improvement opportunities and inform future nursing education programming.
- Coordinate and support AI\-related programs, initiatives, and special projects across Nursing Programs and enterprise teams.
- Develop and maintain program schedules, deliverables, and documentation to ensure timely execution and alignment with strategic priorities.
- Monitor and execute project timelines, track deliverables, and support compliance with contractual, grant, and organizational requirements.
- Conduct analyses of program components, including AI initiatives, to support decision\-making and continuous improvement.
- Support initiatives focused on AI in nursing practice, including the development of educational resources, toolkits, and program materials.
- Lead the development in maintaining and updating AI\-related content, including position statements, web content, and SharePoint resources.
- Conduct and report on environmental scanning and synthesize emerging AI trends, tools, and implications for nursing practice and the workforce.
- Contribute to the development and dissemination of resources that promote AI literacy, safe adoption, and ethical use in nursing.
- Manage and support volunteer groups, boards, and coalitions, ensuring effective coordination, communication, and documentation.
- Serve as a point of contact for assigned internal and external stakeholders, addressing program\-related questions and needs.
- Coordinate and lead meetings, webinars, and events, including scheduling, agendas, materials, and follow\-up actions.
- Build and maintain strong working relationships with partners, consultants, association members, and other stakeholders.
- Prepare presentations, reports, and communications that reflect program strategies, outcomes, and impact.
- Support the development of executive\-level materials, including summaries, dashboards, and data visualizations.
- Communicate findings, updates, and recommendations to internal teams and external stakeholders.
- Identify opportunities to improve processes, workflows, and program efficiency, including the integration of AI\-enabled tools where appropriate.
WHAT YOU BRING TO ANE:
- Eight\-plus years of overall experience in the following areas:
+ Minimum of three (3\) years of experience managing and leading cross\-functional teams to meet aggressive product goals, with sound judgment
+ Demonstrated experience evaluating, implementing, and / or leveraging artificial intelligence, digital health technologies, clinical informatics, data\-driven solutions, or healthcare innovation initiatives
+ Plus, at least five (5\) years of nursing experience is required
- Bachelor's Degree (BA) in nursing
- Registered Nurse (RN), with an active, unrestricted license
- Intermediate to expert knowledge of MS Office, including Word, Excel, PowerPoint, Outlook and SharePoint.
- Demonstrated relationship\-management skills with healthcare leaders, partners, boards, government entities, and funders.
- Ability to create, implement, and modify processes.
- Expert written and verbal communication skills, with strong attention to detail.
- Understanding of AI applications in healthcare, including risks, the regulatory landscape, ethical considerations, and safety implications.
- Ability to proofread and manage multiple priorities in a fast\-paced environment with changing demands.
- Excellent organizational and customer service skills.
- Ability to recognize cultural differences and political sensitivities when interacting with internal and external consultants, vendors, association members, and other stakeholders.
What ANE Offers You:
- Join us and support more than 5 million Registered Nurses in the United States.
- Every role within ANE contributes to a healthier world through The Power of Nurses™.
- An opportunity to help transform a 130\-year\-old organization to meet the future needs and demands within Health Care.
- Be a role model for embracing and empowering the uniqueness of every employee.
- Continuously innovating through creative and strategic initiatives.
- Exceptional benefits including but not limited to 401K retirement contributions of up to 7%, generous PTO which includes the week\-off between Dec 25 and Jan 1, in addition to Personal Days\-off, 11 paid Holidays, excellent health/medical benefits, and much more.
Commitment to your career development and advancement through ANE learning and development programs (internally and externally).
*
Work Schedule:Hybrid employees must work a minimum of 20% in the office.
Location:ANE Headquarters office located at: 8403 Colesville Road, Suite 500, Silver Spring, MD 20906
Learn more about the American Nurses Enterprise:https://www.nursingworld.org/ana\-enterprise\-jobs/
https://www.linkedin.com/company/american\-nurses\-association
The American Nurses Enterprise:Founded in 1896, the American Nurses Enterprise is the family of nonprofit organizations that comprise of the American Nurses Association (ANA), the American Nurses Credentialing Center (ANCC), and the American Nurses Foundation (ANF).
Equal Opportunity Employer:The ANE is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At American Nurses Association, Inc, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
American Nurses Association, Inc AI Hiring
American Nurses Association, Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Silver Spring, MD, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.