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About This Role
Overview:
Be inspired. Be rewarded. Belong. At Emory Healthcare.
At Emory Healthcare we fuel your professional journey with better benefits, valuable resources, ongoing mentorship and leadership programs for all types of jobs, and a supportive environment that enables you to reach new heights in your career and be what you want to be. We provide:
- Comprehensive health benefits that start day 1
- Student Loan Repayment Assistance & Reimbursement Programs
- Family-focused benefits
- Wellness incentives
- Ongoing mentorship, *development,* and leadership programs
- And more
Work Location: Atlanta, GA
Description:
- Plays a key role in protecting the financial stability of Emory University, Emory Healthcare, and its wholly owned insurance subsidiary, Clifton Casualty Insurance Co. Ltd. (CCIC)
- Processes and monitors claim related payments and assures monthly reconciliation for EHC, CCIC, and its actuary
- Manages the monthly indemnity and legal expense payment process for Medical Professional, General Liability and Network Security & Privacy matters payable under the Emory Liability Insurance Program through CCIC
- Tracks and creates monthly loss runs which tracks all payment and reserve related movement for third party liability matters insured under CCIC
- This role also has claims data analytics components, responsibility for analyzing insurance claims data for reporting purposes, to find trends, and support decisions
- The Claim Financial Process Manager will collect data, perform statistical analysis, create dashboards/ reports and collaborate with teams including Claims, Risk, Patient Safety, Quality, and Clinical operating units
- The goal is to bring awareness to claims holistically to help inform risk, patient safety and quality initiatives, ultimately to improve patient outcomes and reduce claims and claim costs
- Management of Financial processes for CCIC:
+ Manages time-sensitive monthly financial data processes, including inputting initial reserves and reserve changes, case settlements, legal and related vendor invoices, monthly accounting reconciliation and balancing, and fiscal year end closing
+ Responsible for running and balancing the monthly loss runs for CCIC
- Data Analysis & Reporting:
+ Collect, validate, normalize, and analyze medical professional and general liability claim data
+ Develop, maintain, and run standard and ad hoc reports, including but not limited to monthly loss runs, transaction reports, and trend and severity analyses
+ Update tower erosion and exhaustion reports to track aggregate losses, paid/incurred amounts, and remaining limits across coverage layers
+ Work with actuarial team to support actuarial analyses by preparing clean, accurate datasets for loss projections, reserve studies, and pricing evaluations
+ Translate complex insurance and claims data into clear, actionable insights for leadership, risk management, legal, and finance stakeholders
+ Respond to requests for specific data reports, such as claims data Medicaid and Medicare applications
+ Develop dashboards and visual reports to track loss performance, emerging risks, and program effectiveness
+ Identify opportunities to improve reporting efficiency, automation, and data accessibility
+ Use data to support strategic risk mitigation initiatives and loss prevention efforts
+ Assist in developing key performance indicators (KPIs) and metrics for captive operations
+ Support internal and external audits by providing accurate, timely, and well-documented data
+ Support system upgrades, data migrations, and enhancements related to reporting and analytics; for the claims management system, currently RLDatix
- Reinsurance & External Carrier Reporting:
+ Work with VP, Insurance, captive manager, and insurance broker to track reinsured losses, recoverables, ceded premiums, and reinsurer participation by layer
+ Support reconciliation of reinsurance payments and recoverables with finance and accounting teams
+ Respond to reinsurer data requests, audits, and ad hoc reporting needs
- Regulatory & Statutory Reporting:
+ Manage and support MMSEA (Medicare Secondary Payer) Section 111 reporting, including:
- a. Data extraction and validation
- b. Timely and accurate submissions to CMS
- c. Resolution of CMS errors, rejects, and compliance issues
+ Support National Practitioner Data Bank (NPDB) reporting by ensuring data accuracy, completeness, and compliance with reporting thresholds and timelines
- Additional Duties as Assigned
- Travel: Less than 10% of the time may be required
- Work Type: Hybrid employee - splits time between working remotely and working in the office
MINIMUM REQUIRED QUALIFICATIONS:
- Education: Bachelor's degree in a business-related field
- Experience: Minimum five years relevant experience
Knowledge, Skills & Abilities
- Knowledge of legal and insurance claims handling principles
- Basic accounting skills, ability to navigate various data systems and strong data skills
- Proficiency with data analysis tools, SQL, BI software (e.g., Tableau, Power BI), and statistical techniques
- Presentation skills
JOIN OUR TEAM TODAY! Emory Healthcare (EHC), part of Emory University (EUV), is the most comprehensive academic health system in Georgia and the first and only in Georgia with a Magnet® designated ambulatory practice. We are made up of 11 hospitals-4 Magnet® designated, the Emory Clinic, and more than 425 provider locations. The Emory Healthcare Network, established in 2011, is the largest clinically integrated network in Georgia, with more than 3,450 physicians concentrating in 70 different subspecialties.
Additional Details:
Emory is an equal opportunity employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by state or federal law.
Emory Healthcare is committed to providing reasonable accommodations to qualified individuals with disabilities upon request. To request this document in an alternate format or to request a reasonable accommodation, please contact Emory Healthcare’s Human Resources at careers@emoryhealthcare.org. Please note that one week's advance notice is preferred.
Salary Context
This $99K-$122K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Emory Healthcare, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($111K) sits 28% below the category median. Disclosed range: $99K to $122K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Emory Healthcare AI Hiring
Emory Healthcare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $122K - $122K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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