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About This Role
The Opportunity
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Health systems and health plans are deploying inbound AI at scale. Hyro, Orbita, Nuance, Notable \- everyone's building in this space. But there's no standard. No one's defined what *good* looks like in healthcare inbound.
Hippocratic AI is positioned to own that. We have the technology, the customers, and the trust. What we need is someone to define the playbook \- the methodology that becomes the industry standard for inbound AI in healthcare.
You're going to build that system. You'll work with our best customers, synthesize what works, and create the frameworks that scale us from individual successes to a repeatable, differentiated offering. The methodology you build becomes how Hippocratic AI wins in inbound for the next five years.
This is a Solutions Architect, Inbound AI Deployments role. You're not executing deployments; you're defining how everyone else executes them. You're writing the playbook that our deployment strategists and engineers will follow for the next 50 customer engagements. You're designing the repeatable system that scales us from one\-off successes to a replicable, differentiated offering.
This role reports directly to the CPO. You'll split your time between health system partnerships (understanding what works in the real world), product engineering collaboration (ensuring what you design is buildable), and internal enablement (teaching your frameworks to the deployment team).
What Success Looks Like in Year One
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- Inbound AI Methodology Defined: You've synthesized best practices from 5\+ health system deployments into a clear, documented methodology for inbound AI in healthcare. This becomes the Hippocratic AI standard—how we approach every inbound deployment from discovery forward.
- Repeatable Configuration Framework: You've designed the configuration architecture (decision tree patterns, IVR logic flows, provider directory mapping, escalation rules) that deployment strategists and FDEs will use repeatedly. It's documented, tested, and proven across 3\+ customer deployments.
- Deployment Playbook: You've codified the end\-to\-end deployment model: discovery, configuration, testing, go\-live, iteration. This becomes the playbook that deployment teams execute on every inbound engagement.
- Customer Learnings Synthesized: You've worked hands\-on with 5\-7 health systems and health plans on their inbound AI deployments. You've labeled decision trees, configured IVR logic, and validated configurations against real patient call patterns. You understand what works, what doesn't, and why.
- Product Requirements Translated: You've translated patient access workflows into crisp product requirements and configuration standards. You've submitted 3\-5 feature requests to the Front Door product team—each one addressing a scaling constraint you discovered in the field.
- Engineering Partnership Established: You've built credibility with the Front Door product and engineering teams. They see you as the voice of inbound deployment reality. Product roadmap priorities reflect your input.
- Enablement \& Documentation: You've created the frameworks, decision trees, configuration templates, and documentation that deployment strategists and FDEs will use to execute inbound deployments without you. Training is documented. Patterns are replicable.
- Competitive Differentiation: You've identified what makes Hippocratic AI's inbound approach different/better than Hyro, Orbita, Nuance, Notable, Fabric. You've articulated this in customer conversations and product strategy.
- Scaling Signal: Your methodology and frameworks have been used by 3\-4 other deployment team members. They're not coming back to you with "how do I...?" questions; they're following your playbook and executing successfully.
Your Core Responsibilities
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- Inbound AI Methodology \& Framework Design: Define the core methodology for inbound AI configuration, decision tree architecture, IVR logic, and provider directory setup. This becomes the standard the entire organization follows. Design repeatable patterns for call center workflows, patient access scenarios, and escalation logic that apply across different health systems and health plans. Document your methodology in clear, usable frameworks that deployment teams can execute without continuous guidance from you. Continuously refine the methodology based on learnings from new deployments and customer feedback.
- Customer Implementation \& Requirements Translation: Partner directly with 5\-7 health system and health plan customers on inbound AI deployments. Spend significant time in configuration sessions, validation work, and go\-live support. Translate patient access, call center, and inbound workflows into crisp product requirements and configuration standards that scale beyond individual deployments. Identify where your methodology breaks or needs refinement. Feed learnings back into the framework. Validate that configurations perform correctly against real patient call patterns and business metrics.
- Product \& Engineering Partnership: Work closely with the Front Door product and engineering teams to ensure the methodology you define is technically sound and buildable at scale. Identify product gaps, configuration constraints, and feature requests based on deployment learnings. Prioritize ruthlessly—what unlocks the most scaling? Influence product roadmap priorities based on deployment reality (not just customer requests). Collaborate on product design for inbound features; ensure they're built with deployment scalability in mind.
- Deployment Team Enablement: Codify your methodology into frameworks, playbooks, decision tree templates, and training materials that deployment strategists and FDEs will rely on. Train deployment team members on your methodology, frameworks, and best practices. Create documentation that answers "how do I configure X?" so deployment teams can execute independently. Build a knowledge base and community of practice around inbound deployments.
- Strategic Vision \& Competitive Positioning: Define what makes Hippocratic AI's inbound approach differentiated vs. competitors (Hyro, Orbita, Nuance, Notable, Fabric). Articulate the strategic positioning of our inbound solutions to customers and internally. Identify white space and expansion opportunities in the inbound market (new use cases, new customer types).
What You Bring
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### Must\-Have
- BA/BS degree required.
- Domain expertise in patient access, call center operations, or inbound workflows. You understand what makes a good call center configuration, what drives call routing decisions, how IVR impacts patient experience, why provider directories matter.
- 8\+ years in inbound/patient access technology or healthcare operations. You've spent significant time in or around call centers, patient access workflows, IVR systems, or inbound healthcare technology. You understand the pain.
- Deep hands\-on experience with inbound AI or contact center automation. You've personally configured IVR systems, designed decision trees, or deployed call center AI solutions. You've labeled data, tested configurations, validated performance. You know how to make it work in practice.
- Proven track record building repeatable systems. You don't just execute one\-off projects. You've built frameworks, playbooks, or methodologies that let other people execute consistently after you. People use your work; they don't reinvent it.
- Full\-lifecycle ownership mindset. You're comfortable owning the entire deployment lifecycle—from customer discovery through configuration, testing, go\-live, and iteration. You don't hand off; you see it through.
- Product thinking with operational execution. You think strategically about what should be built and how to scale. You also know how to get into the weeds—configuring systems, testing edge cases, debugging issues.
- Communication and persuasion skills. You can explain complex inbound workflows to non\-technical customers. You can articulate product requirements to engineers. You can align teams around a methodology.
Nice\-to\-Have
- Hands\-on experience with inbound digital front door platforms (Hyro, Orbita, Nuance, Syllable, Notable, Fabric).
- Background in healthcare operations, health plans, health systems, or patient access.
- Experience building configuration tools or internal frameworks that enable others to execute consistently. Familiarity with healthcare data standards (FHIR, HL7\) and EHR integration points relevant to inbound workflows.
- Prior experience at healthcare AI/SaaS companies or forward\-deployed organizations.
- Track record of mentoring and enabling other operators to execute frameworks you've built.
Why Hippocratic AI
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- You're joining the future of safe, clinical AI. Hippocratic AI is the only generative AI platform built from the ground up for safe, autonomous clinical conversations. Our 99\.9%\+ accuracy has been earned through rigorous clinical training and validation.
The team is world\-class. Founded by healthcare innovators, physicians, and AI pioneers from El Camino Health, Johns Hopkins, Washington University, Stanford, Google, Meta, Microsoft, and NVIDIA. You'll work alongside people who've spent careers transforming healthcare.
- You're backed by the best. Recent $126M Series C at $3\.5B valuation, led by Avenir Growth, with support from CapitalG, General Catalyst, a16z, Kleiner Perkins, and leading healthcare systems. We have the resources and runway to execute our vision.
- You're defining a new category. Inbound AI in healthcare is nascent. There's no standard playbook. You're not optimizing someone else's system; you're creating the system that becomes the industry standard. That's rare.
- You partner with world\-class teams. You'll work alongside experienced deployment strategists, forward\-deployed engineers, product leaders who understand healthcare, and AI researchers pushing model boundaries. You'll learn as much as you teach.
- The platform actually works. You're not trying to make a mediocre AI system work in healthcare. Hippocratic AI has 99\.9%\+ accuracy and trust from leading health systems. You're building methodologies on top of something that already works.
*Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @**hippocraticai.com* *email addresses. We will never request payment or sensitive personal information during the hiring process.*
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 Hippocratic AI, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Hippocratic AI AI Hiring
Hippocratic AI 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 $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.
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