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About This Role
At Curana Health, we're on a mission to radically improve the health, happiness, and dignity of older adults—and we're looking for passionate people to help us do it.
As a national leader in value\-based care, we offer senior living communities and skilled nursing facilities a wide range of solutions (including on\-site primary care services, Accountable Care Organizations, and Medicare Advantage Special Needs Plans) proven to enhance health outcomes, streamline operations, and create new financial opportunities.
Founded in 2021, we've grown quickly—now serving 200,000\+ seniors in 1,500\+ communities across 32 states. Our team includes more than 1,000 clinicians alongside care coordinators, analysts, operators, and professionals from all backgrounds, all working together to deliver high\-quality, proactive solutions for senior living operators and those they care for.
Ranked \#147 on the Inc. 5000 list of America's fastest\-growing private companies, we're just getting started. If you're looking to make a meaningful impact on the senior healthcare landscape, you're in the right place—and we look forward to working with you.
For more information about our company, visit CuranaHealth.com.
Summary
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Curana Health is seeking a Senior Technical Project Manager (AI/IT) to lead complex enterprise technology and AI\-enabled initiatives that support critical business priorities across the organization. This strategic role is responsible for driving the successful delivery of high\-impact programs spanning data, applications, integrations, security, and emerging AI technologies while partnering closely with leaders across both business and technology functions.
The Senior Technical Project Manager (AI/IT) will oversee the planning, execution, and delivery of complex technology initiatives, with a strong emphasis on AI\-enabled solutions within a regulated healthcare environment. This role is accountable for end\-to\-end program execution, ensuring projects align with business objectives, enterprise architecture standards, and regulatory requirements, including HIPAA and PHI compliance.
Working cross\-functionally with business leaders, engineering, data science, architecture, product, and IT teams, this individual will help translate strategic initiatives into executable roadmaps and deliver scalable, secure, and compliant healthcare technology solutions. The role requires a strong blend of technical project management expertise, stakeholder leadership, and the ability to navigate complex, matrixed environments.
The ideal candidate is an experienced technical project leader with a proven track record of managing multiple high\-impact initiatives simultaneously, influencing stakeholders at all levels, and driving results in fast\-paced environments. Success in this role requires strong executive communication skills, excellent problem\-solving abilities, and the capability to bridge business strategy with technology execution.
Essential Duties \& Responsibilities
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- Lead the delivery of complex technology programs across data, applications, integrations, security, and AI\-enabled solutions, ensuring projects are completed on time, within scope, and aligned to business objectives.
- Define and manage project plans, schedules, resource allocations, budgets, risks, dependencies, and key milestones across multiple concurrent initiatives.
- Coordinate cross\-functional teams and proactively manage interdependencies across engineering, data, product, security, architecture, and business functions.
- Facilitate project intake, prioritization, planning, execution, monitoring, and closure activities throughout the delivery lifecycle.
- Drive stakeholder engagement and support executive decision\-making through clear communication, status reporting, and risk management.
- Establish and monitor program success metrics, business outcomes, adoption, and return on investment.
- Identify, assess, and mitigate delivery risks related to data quality, system integrations, security, regulatory compliance, and operational readiness.
- Ensure adherence to privacy, security, governance, and healthcare regulatory requirements, including HIPAA and PHI standards.
- Facilitate solution reviews, trade\-off discussions, and go/no\-go decisions to support successful deployments and releases.
- Maintain project and governance documentation, including requirements, testing, validation, support, and implementation materials.
- Support the continued development and evolution of project governance frameworks, AI delivery standards, and best practices that promote consistency, compliance, and scalability across the organization.
- Align project priorities, resource planning, and delivery activities with organizational strategy while supporting disciplined execution and proactive risk and issue management.
Qualifications
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Required Qualifications:
- Bachelor’s degree in a related field or equivalent combination of education and experience required.
- Minimum of five (5\) years of experience leading AI/ML, advanced analytics, digital transformation, or complex enterprise technology initiatives with demonstrated involvement in AI\-enabled solutions and technologies.
- Healthcare industry experience or healthcare domain knowledge.
- Experience delivering technology initiatives within regulated healthcare environments, including a strong understanding of HIPAA and PHI compliance requirements.
- Experience partnering with technical teams to support the delivery, implementation, or operationalization of AI/ML solutions, including an understanding of the AI lifecycle from development and validation through deployment and monitoring.
- Demonstrated experience delivering externally facing digital healthcare solutions, including:
+ Patient portals
+ Provider/facility portals
+ Healthcare web applications
- Proven experience leading initiatives involving:
+ Authentication and identity management solutions (SSO, IAM)
+ Data models and data architecture
+ API\-driven integrations and services
+ AI\-enabled and data\-driven business solutions
- Demonstrated success leading cross\-functional initiatives involving data, applications, infrastructure, security, and business stakeholders.
- Experience managing security, compliance, and data governance considerations within AI and digital initiatives.
- Proven ability to lead multiple concurrent, high\-impact initiatives with complex interdependencies and competing priorities.
- Strong executive communication skills, with the ability to translate complex technical and AI concepts into clear business value.
- Demonstrated expertise in stakeholder management and alignment across business and technology organizations.
- Deep understanding of project and program management methodologies, including Agile, Waterfall, and hybrid delivery approaches.
- Strong understanding of architecture principles, system integrations, data models, APIs, and identity/authentication concepts.
- Strong analytical, problem\-solving, and decision\-making capabilities.
- Proficiency with project management and collaboration tools such as Jira, Confluence, Microsoft Project, SharePoint, and similar platforms.
- If not currently certified, the ability to obtain a recognized project management certification within one year of hire is required.
Preferred Qualifications:
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- Familiarity with AI tools and platforms such as Copilot and Claude, as well as enterprise systems including Salesforce and Workday.
- Prior experience working in fast\-paced, highly dynamic environments.
- Project Management Professional (PMP) certification or other project management certification(s).
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 Curana 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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.
Curana Health AI Hiring
Curana Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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
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