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About This Role
About VisiQuate
VisiQuate is a healthcare revenue cycle intelligence company. We help health systems and revenue cycle services organizations improve financial performance with analytics, automation and AI. We are building a new generation of AI-native products that work inside our clients' revenue cycle workflows, and we are growing the team that builds them.
The role: run your service line like a general manager
You will own a portfolio of AI-native revenue cycle products as if it were your own business. That means more than features and a backlog. You are accountable for whether your service line wins in the market, delivers cash outcomes for clients, and grows profitably.
In practice, you are the person in the company who knows your area best:
- The market, the buyers, and every client problem worth solving
- Every competitor, including what Epic and other EHRs are building, and how we beat them
- What it costs us to deliver, and what clients will pay
- Whether each pilot is succeeding, and why
- How Sales should pitch it and how Marketing should frame it
You lead through influence across Engineering, Data Science, Customer Success, Sales and Marketing. Nobody reports to you on those teams, but your service line's results are yours.
What you'll own
Market and clients
- Know your market inside and out: segments, buyers, budgets, buying cycles, and the regulatory and payer changes that reshape it.
- Maintain a living map of client challenges in your area, ranked by the cash and effort at stake, and know which ones we solve today.
- Spend time in client workflows. Build a client advisory group and keep a steady stream of interviews and site visits.
- Be the voice of the client inside VisiQuate, backed by evidence rather than anecdotes.
Competition and ecosystem
- Track every competitor in your space: their products, pricing, wins, losses and direction. Know how we beat each one.
- Know what Epic, Oracle Health and other EHR vendors are building that overlaps with us, and where we complement rather than compete.
- Define how we sell into clients on advanced EHRs like Epic: integration paths, marketplace programs, and the case for VisiQuate alongside native tools.
- Watch payer, clearinghouse and partner moves that create threats or openings. Recommend build, buy or partner when it fits.
- Run win and loss reviews and turn them into roadmap and positioning changes.
Strategy and economics
- Own the vision, strategy and roadmap for your service line, from first release through later versions.
- Build the business case before development, with outcomes measured in cash: cash recovered, avoidable denials, touches per claim, cost to collect, days to resolution.
- Recommend pricing and packaging for approval by the EVP and Growth leadership. Know the value we create per client and what the market will bear.
- Know your cost drivers: AI inference, clearinghouse and transaction fees, data and infrastructure, implementation and support effort. Design products and prices that protect margin.
- Make disciplined trade-offs on client value, differentiation, commercial impact and cost, and say no clearly.
Build and deliver
- Write specifications, business rules, acceptance criteria and non-functional requirements that work for both engineers and AI development tools.
- Keep the Product Roadmap current and tie every development task to a roadmap feature.
- Build on our shared platform and canonical data model, and reuse shared services rather than rebuilding them.
- Lead Agile delivery with your engineering partners: refinement, planning, demos, release and post-launch measurement.
- Modernize existing capabilities into new products while protecting the experience of current clients.
AI quality, safety and compliance
- Set evaluation criteria for AI and agentic features: accuracy, task completion, reliability, latency and cost.
- Define permissions, escalation and human oversight for agent actions on client systems, such as appeals, rebills, payer portals and EHR updates.
- Work with Security and Legal on PHI handling, auditability and BAAs.
Pilots and client outcomes
- Own every pilot in your portfolio end to end: success criteria, data readiness, implementation plan, weekly check-ins and go-live.
- Prove ROI per client in cash, and turn successful pilots into references and case studies.
- Own client outcomes through renewal and expansion with Customer Success, including adoption, escalations and the support playbook.
Go-to-market
- Build demos that client-facing teams use to win, defend and expand accounts.
- Train Sales on how to pitch your products, handle objections and position against competitors. Join strategic deals when it matters.
- Work with Marketing on positioning, messaging, launch plans, case studies and thought leadership.
- Represent your service line externally at client forums, industry events and with analysts.
Business reviews
- Run a regular business review of your service line: pipeline, revenue, margin, adoption, client outcomes and delivery health.
- Report to leadership with clear asks, risks and decisions needed.
How you'll work
You run our Product Development Lifecycle with AI built into every phase and a human accountable at every gate. AI should accelerate the lifecycle, not replace its discipline.
- Use AI for discovery, research synthesis, specifications, prototyping and analysis, and hold its output to enterprise standards.
- Build working demos and prototypes yourself with AI tools such as Claude Code, so ideas reach clients in days rather than months.
- For new products, follow our path from concept pitch to demos, a pilot client, go-live and general availability, with the prospect pipeline built in parallel.
- Keep your service line's product home current in Notion and its roadmap current in GitHub, so anyone in the company can see where things stand.
What success looks like in your first year
- You can brief the executive team on your market, competitors and EHR landscape without notes.
- Your service line has an approved strategy, a business case, and a roadmap tied to cash outcomes.
- At least one pilot is live, measured, and producing a referenceable ROI story.
- Sales can demo and pitch your products confidently without you in the room.
- Pricing reflects both client value and our cost to deliver, and margin is tracked.
- You run a regular business review that leadership uses to make decisions.
Requirements
- Required: 7+ years of hands-on healthcare revenue cycle experience as a subject matter expert, with depth in one or more of: denials and appeals, patient access and financial clearance, payment integrity and underpayments, A/R follow-up, payer contracts and policy.
- 8+ years of Product Management on complex SaaS or enterprise products, with clear ownership of business results. Senior Director candidates: experience leading Product Managers and multiple product lines.
- Working knowledge of 837/835 data, CARC/RARC codes and EHR workflows (Epic, Oracle Health/Cerner).
- Experience with AI/ML, automation or agentic products, and direct use of AI prototyping tools.
- Experience owning pricing, packaging or a business case through to revenue.
- Strong technical fluency to partner with engineers, data scientists and architects.
- Strong understanding of SaaS economics: adoption, retention, expansion and margin.
- Comfort in front of clients, Sales and executives, and a mindset that combines speed with rigor.
Our Founding Principles
Successful candidates will value and embrace VisiQuate's Founding Principles:
- Leadership: We lead by example and by empowering our clients, partners and staff.
- Optimism: Our glass is decidedly half full, and then some.
- Curiosity: We approach the who, what, where, when, why and how questions with an inquisitiveness that delights in discovering fresh ideas.
- Zeal: We approach our work with eagerness, determination and contagious enthusiasm.
- Integrity: We value conscience, character, truth and honor as fundamental principles.
- Innovation: We believe in a continuous and relentless pursuit of all that seems unimaginable.
- Fun: We believe that hard work and fun go hand in hand.
- Compassion: We lead not only with our heads, but with our hearts.
- Giving: We gladly give our time and money to causes we believe in.
Salary Context
This $175K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At VisiQuate, 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 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $175K to $215K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
VisiQuate, Inc. AI Hiring
VisiQuate, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $215K - $215K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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