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About This Role
About Better Health
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Healthcare is entering a new era.
AI will fundamentally change how companies communicate, make decisions, route work, support customers, and operate internally.
We believe the companies that win won’t simply adopt AI tools. They’ll redesign how work gets done.
Better Health is looking for someone to help build that future.
About the Role
==================
As our Senior Manager, AI Enablement \& Business Architecture, you’ll help design Better Health’s AI\-native operating model.
Your mission is not to deploy AI tools or build isolated automations.
Your mission is to identify where AI can create meaningful business leverage, redesign workflows around it, and ensure every AI investment strengthens the business as a whole rather than creating another disconnected point solution.
You’ll partner across Operations, Product, Engineering, Sales, Member Services, Marketing, and Clinical Operations to build the systems, workflows, and governance that allow Better Health to scale significantly faster than headcount.
Our Philosophy
==================
One of the biggest risks companies face in the AI era is solving problems one team at a time.
Sales wants an AI SDR.
Support wants an AI chatbot.
Operations wants workflow automation.
Marketing wants AI\-generated campaigns.
Individually, these ideas may all be valuable.
Collectively, they often create fragmented experiences, duplicated effort, inconsistent data, disconnected systems, and technical debt.
This role exists to ensure Better Health builds AI capabilities as an integrated operating model rather than a collection of independent AI projects.
Your job is not to build AI solutions.
Your job is to build an AI\-native business architecture.
What You’ll Own
===================
AI Strategy \& Business Architecture
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Develop Better Health’s AI roadmap by identifying, evaluating, prioritizing, and implementing opportunities where AI can create measurable business value.
Examples include:
- AI\-powered lead scoring and prioritization
- Communications orchestration
- Intelligent routing
- AI copilots
- Next best action recommendations
- Workflow automation
- Operational decision support
- AI\-enabled quality assurance
You’ll evaluate opportunities based on business impact, not technical novelty.
AI Workflow Orchestration
-----------------------------
Design how humans and AI work together.
You’ll determine:
- What should AI observe?
- What should AI decide?
- What should AI automate?
- When should humans stay in the loop?
Examples include:
- AI classifies inbound emails, calls, texts, and chats.
- AI routes work to the right team.
- AI drafts responses for review.
- AI prioritizes leads.
- AI surfaces operational bottlenecks.
- AI recommends next best actions.
Your goal isn’t replacing people.
Your goal is ensuring people spend more time on work that requires judgment, empathy, creativity, and relationship building.
Business Architecture \& Systems
------------------------------------
Understand how work flows across Better Health and redesign it for scale.
You’ll optimize the company as a system, not as a collection of departments.
This includes:
- Customer journeys
- Communication architecture
- Business systems
- Workflow automation
- System integrations
- Routing logic
- Cross\-functional workflows
- Operational dashboards
You’ll constantly ask:
- Are we solving the company problem or one team’s problem?
- Can this capability serve multiple teams?
- Are we creating another silo?
- How would we redesign this process if we started from scratch today?
Observability \& Opportunity Prioritization
-----------------------------------------------
Build the visibility and operating framework that allows Better Health to consistently invest in the highest\-impact opportunities.
You’ll establish how we:
- Observe opportunities
- Measure them
- Build business cases
- Prioritize investments
- Measure outcomes
Every initiative should have:
- A clearly defined problem
- Measurable success metrics
- Expected business impact
- An implementation roadmap
- A clear ROI hypothesis
Example Projects
====================
In your first year, you might:
### Build an AI Communications Layer
Rather than implementing separate AI solutions for Sales, Operations, and Member Services, design a unified AI layer that understands intent, recommends next best actions, and intelligently routes work across the company.
### Develop Intelligent Lead Scoring
Build an AI\-assisted scoring model that predicts conversion, prioritizes outreach, and recommends follow\-up actions.
### Redesign the Member Journey
Map communications across Freshsales, Customer.io, Genesys, AfterShip, and future platforms, then create a unified, measurable customer lifecycle.
### Build an Operations Copilot
Create an internal AI assistant that helps teams answer questions, summarize customer history, recommend actions, surface risks, and automate repetitive work.
What Success Looks Like
===========================
Within your first 12 months, you will have:
- Built Better Health’s AI opportunity roadmap.
- Instrumented key customer and operational workflows.
- Launched AI\-enabled workflows with measurable business impact.
- Reduced manual work through intelligent automation.
- Created reusable AI capabilities that serve multiple teams.
- Established a repeatable framework for evaluating and prioritizing AI opportunities.
- Helped define Better Health’s AI\-native operating model.
Experience \& Qualifications
================================
We recognize this is a non\-traditional role, and we don’t expect candidates to check every box.
Strong candidates will likely have experience across several of the following areas.
### Professional Experience
- 6\+ years in Business Systems, Product Operations, Revenue Operations, Strategy \& Operations, Solutions Architecture, Management Consulting, or similar roles.
- Experience leading complex cross\-functional initiatives.
- Experience designing scalable business processes.
- Experience implementing automation and systems integrations.
- Experience evaluating or deploying AI solutions in business environments.
- Experience partnering with Product, Engineering, Operations, Sales, and Customer Success teams.
### Technical Skills
- Strong understanding of modern AI capabilities, LLMs, and agentic workflows.
- Familiarity with workflow automation platforms and AI tooling.
- Experience with CRM, customer engagement platforms, and business systems.
- Experience working with APIs and systems integrations.
- Strong analytical and problem\-solving skills.
### What Makes Someone Successful
You are:
- A systems thinker who optimizes businesses, not departments.
- Comfortable operating in ambiguity.
- Curious, analytical, and highly collaborative.
- Able to translate business problems into scalable technical solutions.
- Passionate about redesigning how companies operate using AI.
We are not looking for the person who knows the most about AI.
We are looking for the person who knows how to redesign a business using AI.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Better 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Better Health AI Hiring
Better Health 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 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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