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About This Role
Location Work from Home Job Category Business Strategy and Innovations Schedule Days Work Type Full time Department Artificial Intelligence Division Date posted 07/16/2026 Job ID R\-99357
### Job Summary
The Director of AI Evaluation owns how Geisinger defines, proves, and sustains quality across its entire AI portfolio — internally built models and vendor\-provided systems alike. This is a hands\-on technical leader who also manages the people who do the building and the proving: the data scientists who develop production machine learning and fine\-tuned AI systems, and the senior analysts who evaluate them. Every high\-value AI initiative — bought or built — needs a single, credible standard for what constitutes quality, who validates it, and how it stays good in production. The Director sets that standard, leads the team that enforces it, and reports findings to the VP of AI, executive leaders, and the board. This role is a manager who develops a multidisciplinary team, a technical authority who defines evaluation method across the enterprise, and a quality owner who guides every major AI program toward evidence that withstands scrutiny.
### Job Duties
- Reports to the VP of AI; directly career\-manages the data science line and matrix\-manages the Senior Analysts, AI Evaluation.
- Determines the quality standard for any high\-value AI initiative at Geisinger — internally built or vendor\-provided — from design through production.
- Holds bought systems to the same standard as built ones, generating local evidence on whether a tool works here for Geisinger's clinicians and patients rather than accepting vendor aggregate or cherry\-picked results.
- Owns the methodology that holds initiatives to that standard: pre\-production validation and live production monitoring.
- Owns the health of the data science team — attracting and retaining strong technical talent, developing careers, and keeping the bench deep, engaged, and growing — and leads and develops the evaluation team alongside it.
- Provides hands\-on technical guidance to program teams as they design validation studies, equity audits, monitoring plans, and escalation playbooks.
- Owns the evaluation toolkit and reusable playbooks and templates that let each new program move faster than the last.
- Translates program\-specific failure modes into concrete, measurable production\-monitoring metrics; defines what is measured and how, while the AI Platform team builds the backend.
- Tracks AI System Performance — the single most important accuracy indicator for each system, against thresholds set to clinical tolerance.
- Tracks User Adoption — engagement, override rates, and time\-to\-action — distinguishing genuine workflow misalignment and alarm fatigue from poor predictive value.
- Connects each AI to the Outcome it was deployed to improve (mortality, time\-to\-treatment, boarding time, denial rate, cost per case) against a pre\-launch baseline over a use\-case\-appropriate horizon, holding both tangible returns and harder\-to\-quantify value in view.
- Monitors Equity — the maximum performance gap on the Pillar 1 metric across the subgroups that matter for the initiative, so disparate impact surfaces early.
Work is typically performed in an office environment. Accountable for satisfying all job specific obligations and complying with all organization policies and procedures. The specific statements in this profile are not intended to be all\-inclusive. They represent typical elements considered necessary to successfully perform the job.
### Position Details
Required Skills and Qualifications:
- People\-leadership experience — managing, developing, and growing technical staff; building teams, not just leading projects.
- Strong foundation in experimental design and causal inference, with judgment about which method fits which situation.
- Hands\-on experience designing and running model evaluation studies in real production settings.
- Experience evaluating LLM or generative AI systems, or comparable experience with complex ML systems where ground truth is ambiguous or noisy.
- Proven ability to translate ambiguous failure modes into concrete, defensible evaluation designs and monitoring metrics.
- Strong fluency in Python and SQL; working comfort with modern ML tooling and cloud\-native data environments.
- Experience in evaluating fairness and equity in ML systems.
- Clear written communication — the role produces evaluation memos and specifications that non\-technical decision\-makers rely on.
- Healthcare, clinical, or regulated\-industry experience strongly preferred.
### Education
Bachelor's Degree\-Related Field of Study (Required)
### Experience
Minimum of 8 years\-Related work experience (Required), Minimum of 3 years\-Managerial/Supervisory (Required)
### About Geisinger
Founded more than 100 years ago by Abigail Geisinger, the system now includes ten hospital campuses, a 550,000\-member health plan, two research centers and the Geisinger Commonwealth School of Medicine. With nearly 24,000 employees and more than 1,700 employed physicians, Geisinger boosts its hometown economies in Pennsylvania by billions of dollars annually. Learn more at geisinger.org( opens in new window) or connect with us on Facebook( opens in new window), Instagram( opens in new window), LinkedIn( opens in new window) and Twitter( opens in new window).
### Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category under applicable federal, state, and local laws.
### Our Vision \& Values
Everything we do is about making better health easier for our patients, our members, our students, our Geisinger family and our communities.
KINDNESS: We strive to treat everyone as we would hope to be treated ourselves.
EXCELLENCE: We treasure colleagues who humbly strive for excellence.
LEARNING: We share our knowledge with the best and brightest to better prepare the caregivers for tomorrow.
INNOVATION: We constantly seek new and better ways to care for our patients, our members, our community, and the nation.
SAFETY: We provide a safe environment for our patients and members and the Geisinger family.
### Our Benefits
We offer healthcare benefits for full time and part time positions from day one, including vision, dental and prescription coverage.
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 Geisinger, 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. Director-level AI roles across all categories have a median of $272,150.
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.
Geisinger AI Hiring
Geisinger has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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