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About This Role
June 12, 2026
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About Our Company
At Healthfuse, we are a dynamic team that ensures hospitals across the nation remain at peak performance, empowering healthcare leaders to focus on what truly matters—building healthier communities. How do we make this happen? By innovating the way healthcare organizations manage their vendors and strategize their revenue cycles. Every day we tackle challenges head\-on through a blend of technology, analytics, and service to drive results. We transform complex issues into opportunities for growth.
Join us and be part of a forward\-thinking crew that values your fresh ideas and energy. At Healthfuse, you are not just starting a job; you are kickstarting a career that makes a real difference. Ready to fuse your passion with purpose? Let us shape the future of healthcare together.
What You’ll Love About Working at Healthfuse
Innovate in Healthcare: Join a highly regarded company with 13\+ years of experience serving 300\+ hospitals. Be part of an ever\-evolving culture where your work directly contributes to positive change in the industry and community.
Grow Your Career: Dive into a fast\-paced and high\-growth field. Enjoy ample opportunities for professional development and the chance to interact with amazing teammates.
Enjoy the Perks: Benefit from a competitive package that includes a competitive salary, bonus opportunity, comprehensive health benefits, a 401k with company match, share program participation, and generous time off to recharge.
Job Summary
The Product Owner, AI \& Data Solutions is responsible for identifying, defining, and prioritizing product and workflow opportunities across the business. This role partners closely with stakeholders, Business Intelligence, and technical teams to translate business needs into clear requirements, maintain delivery\-ready priorities, and help shape scalable solutions.
This is a product ownership\-led role. The core value of this position is in understanding business problems, clarifying priorities, building alignment, and translating needs into actionable work that drives execution. In addition to strong product ownership capability, the ideal candidate brings enough technical and AI fluency to support design discussions, improve workflows, and help identify practical opportunities for AI and automation.
This role is ideal for someone who enjoys turning businesses or client problems into structured technology solutions, thrives in small cross\-functional teams, and is energized by healthcare, data, system thinking, and applied AI.
Desired Qualifications
Experience in healthcare, healthcare technology, or revenue cycle management
Familiarity with BI and analytic concepts such as dashboards, KPIs, datasets, ETL, reporting workflows, and data models
Comfort with tools such as SQL, Power BI, Tableau, Jira, workflow tools, or low\-code/no\-code tools
Experience identifying or applying AI and automation tools in practical business settings
Experience supporting user acceptance testing, rollout readiness, adoption, or internal enablement
Experience working in small, collaborative, cross\-functional teams
Core Responsibilities
Product Ownership \& Prioritization:
Own or support intake, discovery, backlog refinement, and prioritization across product, BI, and technical initiatives
Gather and clarify business needs and translate them into requirements, workflows, user stories, and acceptance criteria
Maintain visibility into priorities, risks, dependencies, and tradeoffs
Help sequence work based on business value, feasibility, and team capacity
Stakeholder Translation \& Cross\-Functional Alignment:
Serve as a bridge between business stakeholders, BI, and technical terms
Align teams on needs, scope, priorities, and expected outcomes
Translate business and operational challenges intro structured product and technical work
Technical \& Solution Support:
Support discussions involving systems, workflows, integrations, data structures, and solution design
Partner with engineering and BI teams to improve clarity and execution readiness
Assist with process mapping, testing, documentation, and rollout planning as needed
AI \& Automation Opportunity Development:
Identify practical opportunities to apply AI and automation to improve workflows and product capabilities
Evaluate ideas and tools based on business value, usability, scalability, and team fit
Translate AI opportunities into clear, prioritized initiatives
Delivery, Validation \& Adoption:
Support planning, validation, and stakeholder acceptance
Help ensure solutions meet requirements and support adoption through communication and documentation
Process Improvement:
Identify ways to improve how work is requested, prioritized, and delivered
Help develop tools and workflows that improve consistency, transparency, and scalability
Engineering Support \& Solution Design:
Partner with engineering and BI teams on solution design, readiness, and delivery support
Help reduce ambiguity and improve the quality of work handed into execution
Required Qualifications
4\+ years of experience in a product ownership, technical product management, business analysis, business systems, BI/product partnership, or a related role
Proven ability to translate business or client problems into clear requirements and technology\-enabled solutions
Strong experience with requirements gathering, backlog management, prioritization, workflow definition, and acceptance criteria
Demonstrated ability to work cross\-functionally with both technical and non\-technical stakeholders
Strong communication and facilitation skills, including the ability to drive alignment and manage competing priorities
Comfort operating in ambiguity and managing multiple priorities in a fast\-paced environment
Technical fluency in systems, workflows, data, and solution design concepts sufficient to partner effectively with technical teams
Strong ownership, organization, and follow\-through
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Healthfuse, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Healthfuse AI Hiring
Healthfuse has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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