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About This Role
Vatica Health and Cozeva have combined to create a single platform that unites clinician\-enabled risk adjustment and clinical documentation with population\-health and quality analytics across the payer and provider ecosystem. This is a defining moment: we are building an AI\-native organization that improves the accuracy of risk adjustment, closes quality gaps, and reduces clinician burden — driving better outcomes for patients and measurable value for our clients.
The Vice President, Product Management – Risk \& Quality AI Solutions will own the roadmap, and delivery of our AI\-native Risk \& Quality product suite. This is a product leadership role for someone who can set annual strategy, operationalize AI/ML, agentic, and LLM\-driven workflows in a regulated healthcare environment, and champion responsible AI as a first\-class deliverable. You will report to the SVP, Product Management \& AI and partner closely with Engineering, the ML/AI team, Clinical, and Compliance.
This role owns product management for the Risk \& Quality AI Solutions portfolio — product strategy, roadmap, delivery, and AI solution performance. It does not include ownership of the underlying data platform or infrastructure, which sits with our Data organization, though you will partner deeply with that team to ensure your products are built on reliable, well\-governed data.
Responsibilities
Strategy \& Portfolio Ownership
- Own the annual roadmap for the Risk \& Quality AI Solutions portfolio, aligning it to company priorities and to opportunities for new revenue, efficiency, and enhanced client value.
- Partner with business stakeholders and the ML/AI team to identify and prioritize the highest\-impact AI\-native product bets across the combined Vatica and Cozeva product lines.
AI\-Native Product Delivery
- Design and deliver AI\-native solutions that apply agents, OCR, NLP, and LLMs to automate clinical and operational workflows while maintaining the accuracy standards required in a regulated healthcare setting.
- Own the AI solution lifecycle end to end — problem framing, build, evaluation, production monitoring, and retirement — in close partnership with Engineering and ML/AI.
- Define and scale human\-in\-the\-loop review for use cases such as chart abstraction, summarization, and coding assistance, balancing automation with clinical and compliance safeguards.
Performance, Evaluation \& Cost
- Establish evaluation and monitoring frameworks for LLM\- and NLP\-based workflows, using rigorous measurement methodologies (e.g., precision/recall, false positive/negative analysis) and continuous performance monitoring.
- Define and track KPIs for each product and the broader portfolio, tying them directly to client impact and efficiency gains.
- Govern AI\-related costs and evaluate model and engineering effectiveness to ensure a sustainable, scalable solution suite.
Responsible AI Governance
- Serve as a leader in responsible\-AI policy and practice for the Risk \& Quality portfolio, ensuring solutions are transparent, auditable, and compliant with applicable healthcare regulations. (Note: verify the specific frameworks and obligations that apply to your products with Compliance.)
Team Leadership
- Mentor a high\-performing product management team; implement OKRs and drive consistent, predictable delivery.
Requirements
- 10\+ years in data\- or AI\-oriented product management, including a track record of building and launching Risk \& Quality solutions powered by ML/AI.
- Demonstrated success delivering mission\-critical, regulated software products at scale, and experience building and leading product teams.
- Proven experience operationalizing AI\- or data\-driven products in production, including evaluation and monitoring frameworks for LLM or NLP\-based workflows.
- Expertise in measurement and evaluation methodologies (precision/recall, false positives/negatives) and continuous performance monitoring.
- Strong technical fluency — able to work closely with Engineering on system architecture, data pipelines, API design, and integration patterns.
- Experience working within an AI development lifecycle (AI DLC) — from data and model development through evaluation, deployment, monitoring, and iteration.
- Experience leading or operating in blended offshore/onshore teams, including managing delivery across time zones and distributed collaboration.
- Experience working in cross\-functional product/AI/engineering pods, with the ability to align product, data science, and engineering toward shared outcomes.
- Working knowledge of Medicare Advantage, HCC coding, and CMS risk adjustment models, including the transition to the v28 model. (Confirm the exact model versions and phasing you require against current CMS guidance.)
- Excellent narrative communicator, able to influence and align diverse stakeholders across product, engineering, clinical, and compliance.
Preferred Qualifications
- Hands\-on experience integrating frontier AI models (e.g., OpenAI GPT, Anthropic Claude) into production products.
- Experience designing or evaluating LLM\-powered systems for chart abstraction, summarization, or coding assistance.
- Familiarity with evaluation tooling (e.g., LangSmith, Ragas, Phoenix) and human\-in\-the\-loop review systems.
- Familiarity with clinical data interoperability standards (FHIR, C\-CDA, HL7\) and health\-plan integrations.
- Understanding of data governance and PHI security frameworks (SOC 2, HITRUST, HIPAA).
- Background scaling SaaS products for complex workflows across payer and provider ecosystems.
- MBA, MPH, or advanced technical degree (preferred, not required).
Competencies:
Drives Results, Vision, and Purpose
- Ensure daily tasks lead to impactful results that align with Vatica priorities.
- Results\-driven champion for Vatica.
- Adjusts plans as needed to ensure effectiveness.
- Profitability focused, persistent in achieving objectives with a positive track record of exceeding performance.
Plans and Aligns, Resourcefulness
- Plans and prioritizes work to meet the goals of the business.
- Breaks down objectives into plans and actions to achieve significant milestones.
- Anticipates and readily adjusts plans.
- Maintains organization of resources to support efficiency
Communicates Effectively
- Exchanging ideas, knowledge, and data so that the message is received and understood with clarity and purpose.
- Leverages emotional intelligence to adapt to the emotions and intentions of others.
Balances Stakeholders \& Manages Conflict
- Anticipating and balancing the needs of multiple stakeholders and multiple projects.
- Highly prepared and knowledgeable about expectations.
- Delivers fair and flexible response to stakeholder needs.
- Handling conflict situations to resolution effectively and professionally.
- Applies fair decision\-making to balance competing interest mediating any points of abrasion
Strategic Mindset
- Considering future possibilities or roadblocks and creating strategies to drive results.
- Anticipates future trends and implications of decision.
Situational Adaptability \& Flexibility
- Adapts approach in real time to respond to different situations.
- Thinks quickly and readily adapts behavior in the moment.
- High level of versatility.
Benefits WORKING AT VATICA HEALTH ADVANTAGES
Prosperity
- Competitive salary based on your experience and skills – we believe the top talent deserves the top dollar
- Bonus Potential (based on role and is discretionary) – if you go above and beyond, you should be rewarded
- 401k plans– we want to empower you to prepare for your future
- Room for growth and advancement\- we love our employees and want to develop within
Good Health
- Comprehensive Medical, Dental, and Vision insurance plans
- Tax\-free Dependent Care Account
- Life insurance, short\-term, and long\-term disability
Happiness
- Excellent PTO policy (everyone deserves a vacation now and then)
- Great work\-life balance environment\- We believe family comes first!
- Strong supportive teams\- There is always a helping hand when you need it
*The salary for a position is typically determined by multiple factors such as the individual's qualifications, experience, skills, and location. The projected compensation range for the position may vary based on these factors and could range from $200,000 to $235,000 (annualized USD). However, this estimate represents just one aspect of our total compensation package offered.*
Salary Context
This $200K-$235K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vatica 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 $214,900 based on 6,420 positions with disclosed compensation. Disclosed range: $200K to $235K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vatica Health AI Hiring
Vatica Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $235K - $235K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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