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About This Role
Remote, US Nov 2, 2026 Allows Remote Work
Highlights
- Location Remote, US
- Start Date Nov 2, 2026
- Work Location Allows Remote Work
Skills
- Data Platform
- Data Engineering
- Artificial Intelligence
- AWS
- Snowflake
- Strategy
- Leadership
- HealthTech
Description
About MatrixCare
MatrixCare provides software solutions to more than 15,000 providers supporting skilled nursing, senior living and long-term care, life plan communities, and home health and hospice care. A multi-year winner of the Best in KLAS award, MatrixCare is trusted by thousands of facility-based and home-based care organizations to improve provider efficiencies and promote a better quality of life for the people they serve. For more information, visit www.matrixcare.com.
About The Role
The VP, Data/AI Platform owns the enterprise data platform, the artificial intelligence and machine learning agenda, and the shared platform services every product team builds on. As Vice President, you set the strategy for how we turn our most valuable asset, data, into a competitive advantage leveraging AI.
You lead a multidisciplinary organization spanning data engineering, data science and machine learning across the United States and India. You are accountable to the Chief Technology Officer and to the executive leadership team for the reliability and economics of the data platform, for moving artificial intelligence capability from idea into production at a pace the market rewards, and for the governance that makes all of it defensible to customers, auditors, and regulators.
What You'll Do
- Own the enterprise data and artificial intelligence strategy, and translate it into a funded, sequenced roadmap that the executive leadership team and the board can hold you to.Own the target architecture for the data platform, including the cloud data platform, streaming and batch ingestion, the semantic and consumption layers, and the reliability, quality, and cost posture of the whole estate.
- Deliver data products that customers experience directly: benchmarking, operational and clinical insight, and the analytics that make the clinical and financial performance of a provider organization visible and actionable.
- Own the artificial intelligence and machine learning portfolio end to end, from problem selection and model development through evaluation, human in the loop design, production deployment, monitoring, and the measured business outcome each capability is meant to produce.
- Select and stand up the orchestration, tooling, and machine learning operations platform that agentic and generative capability runs on, and hold it to enterprise reliability and cost standards.
- Establish and run artificial intelligence governance: model risk management, data classification, evaluation and guardrail standards, bias and drift monitoring, and the audit evidence that regulated customers expect before they will buy.
- Own shared platform services and the internal developer platform, so product engineering teams ship faster on common capability rather than rebuilding it team by team.
- Own data governance, master data and patient identity, and interoperability of the data layer, so that a patient, a facility, and an episode mean the same thing everywhere in the company.
- Partner with the VP, Product Management and the commercial organization to convert platform capability into packaged, priced offerings, and to choose investment from customer and usage evidence rather than from opinion.
- Own the build, buy, partner, or license decision for artificial intelligence capability, with the sizing, governance, and contractual analysis to support it.
- Manage the domain budget and vendor portfolio, including cloud and data platform spend, with a clear view of unit economics and a bias toward capability that compounds rather than capability that is rented indefinitely.
- Recruit, develop, and retain a high performing, diverse team across distributed locations. Conduct regular one to ones and performance conversations, build succession beneath every leadership seat, and grow the technical bench rather than concentrating expertise in a few people.
What You'll Bring
Required Qualifications:
- Bachelor's degree required. Advanced degree in a quantitative, computer science, or business discipline preferred.
- 12 or more years of relevant experience across data platform, data engineering, data science, and machine learning, including 5 or more years leading leaders.
- Demonstrated accountability for a data and artificial intelligence discipline's strategy, budget, and outcomes at the executive level, ideally within healthcare or another environment where the data carries regulatory weight.
- Hands on fluency, not oversight alone, with a modern cloud data stack. Direct working experience with Snowflake and with AWS data and artificial intelligence services including Bedrock, SageMaker, and Redshift.
- Demonstrated record of moving data science and machine learning work from idea into production quickly, with measurable business results attached rather than models that stopped at the prototype.
- Experience building and operating on protected health information or equivalently regulated data, including data use agreements, data classification, de-identification, and access governance.
- Experience owning a data platform architecture decision and the migration or modernization that followed it, including the operating model that ran it afterward.
- Working knowledge of agentic artificial intelligence and large language model orchestration frameworks, retrieval augmented generation, model evaluation and responsible artificial intelligence guardrails, and human in the loop workflow design.
- Experience leading globally distributed teams, including engineering capability in India.
- Track record of partnering with product and commercial leaders to turn platform capability into revenue rather than into internal tooling.
Preferred Qualifications:
- Prior experience in healthcare technology, electronic health record platforms, or post-acute care across skilled nursing, home health, hospice, senior living, or private duty.
- Familiarity with clinical assessment instruments and the reimbursement models built on them, and with the interoperability standards that move healthcare data, including FHIR and TEFCA participation.
- Experience commercializing data, including de identified data products, benchmarking offerings, and the contractual and privacy work that makes them sellable.
- Experience in a private equity backed or growth-oriented software business, including exposure to a board, audit committee, or sponsor.
- Familiarity with software as a medical device consideration and the decision boundary documentation clinical models require.
- Experience embedding artificial intelligence assisted delivery practices inside an engineering organization, with adoption and impact measured rather than assumed.
- Experience with platform engineering and internal developer platform practices at enterprise scale.
What Success Looks Like
- You translate data and artificial intelligence capability into customer value and commercial outcome rather than into capability slides
- You make quality decisions without incomplete information, then revisit the decision as evidence arrives
- You have built organizational design, workforce planning, and budget ownership at scale
- You have successfully built and mentored your teams across time zones and cultures, and grow successors rather than dependencies
- You govern vendors and delivery partners without ceding technical direction to them
- You balance speed to market against governance, privacy, and clinical safety, and can explain that balance to a customer, an auditor, and a board
- You are a trusted advisor to the Chief Technology Officer, the executive leadership team, and the board
Why Join MatrixCare
At MatrixCare, we are committed to fostering a culture where teammates can grow their careers and make an impact on the healthcare industry. We offer opportunities to contribute to innovative solutions that help improve care delivery, while benefiting from a flexible and supportive work environment. Our teammate value proposition includes comprehensive health and wellness benefits, flexible time off, professional development opportunities, and a collaborative culture built on trust, innovation, and teamwork. By joining MatrixCare, you'll be part of a purpose-driven organization dedicated to advancing healthcare technology and creating an exceptional experience for both teammates and customers.
Compensation
$245,000 - $275,000
We are shaping the future at MatrixCare, and we recognize the need to build on and broaden our existing skills and continue to attract and retain the world's best talent. We work hard to offer holistic benefits packages, provide flexible work arrangements, cultivate a workforce culture that allows employees to grow personally and professionally, and deliver competitive salaries to our team members. This position qualifies for a comprehensive benefits package and individual pay decisions are based on a variety of factors, such as the candidate's geographic work location, relevant qualifications, work experience, and skills. At MatrixCare it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.
Work Environment / Location Expectations
- Remote (US)
Equal Employment Opportunity / Legal Statements
MatrixCare is committed to fostering a diverse, inclusive, and equitable workplace where every individual is treated with respect and dignity. We are an Equal Opportunity Employer and make employment decisions based on qualifications, merit, business needs, and performance, without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, genetic information, or any other characteristic protected by applicable law. We are dedicated to providing equal access to opportunities and reasonable accommodations throughout the recruitment and employment process, ensuring that all teammates can contribute, grow, and succeed in an environment where diverse perspectives are valued and encouraged.
Salary Context
This $245K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Matrixcare, 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 $212,000 based on 11,866 positions with disclosed compensation. This role's midpoint ($260K) sits 23% above the category median. Disclosed range: $245K to $275K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Matrixcare AI Hiring
Matrixcare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $275K - $275K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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