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About This Role
About OpenLoop
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OpenLoop was co\-founded by CEO, Dr. Jon Lensing, and COO, Christian Williams, with the vision to bring care anywhere. Our telehealth support solutions are thoughtfully designed to streamline and simplify go\-to\-market care delivery for companies offering meaningful virtual support to patients across an expansive array of specialties, in all 50 states.
About The Role
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OpenLoop’s mission is to bring care anywhere by powering telehealth solutions at scale.The Director of AI Technologies is a founding leader responsible for building and scaling the organization’s enterprise AI capability. This role owns the AI platform, governance, and delivery of high\-impact use cases across the business. Reporting to the VP of Enterprise Technology, this leader operates across strategy and execution—driving roadmap, architecture, and adoption at scale.
What You’ll Do:
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- Define and execute the enterprise AI roadmap aligned to business priorities
- Identify, prioritize, and deliver high\-impact AI use cases with measurable ROI
- Represent AI strategy in executive and cross\-functional forums
- Own the design, deployment, and operation of the enterprise AI platform
- Establish scalable, secure, and compliant AI infrastructure (LLMs, MLOps, integrations)
- Define standards for model lifecycle management (deployment, monitoring, incident response)
- Build and enforce enterprise AI governance, including model risk, compliance, and acceptable use
- Lead governance reviews and ensure regulatory alignment (e.g., HIPAA)
- Maintain oversight of AI systems, vendors, and usage across the organization
- Lead end\-to\-end delivery of AI solutions across clinical, operational, and corporate functions
- Drive adoption and integration into business workflows
- Ensure outcomes are tied to efficiency, cost, or performance improvements
- Partner across IT, Clinical, HR, Operations, and Data teams
- Influence stakeholders and align AI initiatives to enterprise priorities
- Build and scale a multidisciplinary AI team
- Define roles, structure, and ways of working
- Manage budget, tooling, and external resources
- Other duties as assigned
Who You Are:
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*Required*
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- 10\+ years in technology roles with 4\+ years in a leadership or management capacity
- Demonstrated experience building or scaling enterprise AI/ML platforms in production environments
- Deep understanding of LLM ecosystems, MLOps practices, vector databases, and AI API architectures
- Experience owning AI governance, model risk management, or responsible AI frameworks
- Track record of translating business problems into AI product requirements and delivering solutions
- Strong executive communication skills — able to present complex AI concepts to non\-technical audiences
- Experience operating in regulated industries (healthcare, financial services, or similar) preferred but not required
*Preferred*
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- Experience in healthcare IT, clinical decision support, or HIPAA\-regulated environments
- Familiarity with AWS AI/ML services (SageMaker, Bedrock, Amazon Connect AI capabilities)
- Experience building teams from the ground up in a new function
- Background in workforce management, BPO technology, or contact center AI
- Advanced degree in Computer Science, Data Science, or related field — or equivalent experience
Our Benefits
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In addition, for salaried positions you would also be eligible for:
- Medical, Dental, and Vision plans
- Flexible Spending/Health Savings Accounts
- Flexible PTO
- 401(k) \+ Company Match
- Life Insurance, Pet insurance, and more
Our Company
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We have a relatively flat organizational structure here at OpenLoop. Everyone is encouraged to bring ideas to the table and make things happen. This fits in well with our core values of Autonomy, Competence and Belonging, as we want everyone to feel empowered and supported to do their best work.
Sound like a good fit? We’d love to meet you.
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 OpenLoop 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
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.
OpenLoop Health AI Hiring
OpenLoop Health has 4 open AI roles 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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