Interested in this AI/ML Engineer role at Emory Healthcare?
Apply Now →Skills & Technologies
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
This is a hybrid position requiring one day per week onsite every other week in our Atlanta based office on Northlake Parkway.
Description:
We are seeking an AI Product Strategist to join the cross\-enterprise Emory Digital Artificial Intelligence team, reporting through the Chief Artificial Intelligence Officer for Emory University and Emory Healthcare. This role plays a critical part in supporting the development, implementation, and ongoing enhancement of predictive models and AI\-driven platforms and applications across the enterprise. The AI Product Strategist serves as a product owner, scrum master, and business analyst, partnering with AI leadership, technical teams, and business stakeholders to define product strategy, manage delivery, and drive adoption. This role ensures AI products align with governance and measurement frameworks while delivering measurable operational, clinical, and organizational value.
You will work on a mixture of co\-development and custom dev work while also playing a critical role on vendor assisted initiatives.
This position will work onsite 1 day/week every other week at our Northlake Parkway office location in Atlanta. RESPONSIBILITIES:
Leadership and Product Strategy:
Develop and maintain product strategy and roadmap for one or more AI\-enabled products in collaboration with AI leadership and stakeholders across Emory Digital and business units. Partner with governance and measurement teams to establish and track product KPIs, including ROI, patient outcomes, operational efficiency, and time savings. Plan and support product rollout and adoption activities, including demonstrations and training, in collaboration with AI leadership, product strategy peers, and Emory learning and development teams.
Product \& Project Management:
Create and maintain product and project management deliverables to align stakeholders on vision, future\-state workflows, and strategic objectives. Develop, manage, and prioritize product backlogs, including features, epics, and user stories, in collaboration with scrum team members, AI leadership, and product business owners. Partner with Emory's project and program management teams to ensure alignment with project governance, reporting, and delivery standards.
Business Requirements \& Stakeholder Engagement:
Work with business leaders, team members, and stakeholders to understand pain points in existing technical solutions and manual processes. Gather and document functional requirements for AI applications and products and translate requirements into clear user stories and acceptance criteria. Collaborate with technical development and implementation teams to translate functional requirements into technical requirements and delivery tasks. Coordinate efforts to disambiguate requirements and perform root\-cause analysis of product issues.
Agile Delivery \& Scrum Leadership:
Serve as scrum master for assigned product teams, leading agile ceremonies including sprint planning, reviews, and retrospectives. Promote agile best practices and continuous improvement across product teams. Vendor Management Assist with vendor management activities related to AI products and platforms. Coordinate requests for vendor support as needed to ensure product success.
Communication \& Change Enablement:
Partner with Emory marketing and communications teams to develop and deliver communications announcing product releases, new features, training opportunities, and project outcomes. Support adoption and change management activities to ensure successful implementation and sustained use of AI products. PREFERRED QUALIFICATIONS:* Experience working on Epic AI agent workstreams
- Education Master's degree in Business Administration, Computer Science, Healthcare Administration, or a related field
- Experience: Minimum 3 years of additional relevant experience
- Certifications: Project management, agile, product management, Lean Six Sigma, process design, design thinking, Azure, AWS, Epic, or related certifications
- Knowledge, Skills, and Ability Requirements (Preferred):
+ Knowledge of healthcare industry operations and digital health, including AI\-enabled applications.
+ Familiarity with governance and measurement frameworks for AI initiatives in enterprise environments.
+ Hands\-on experience with AI/ML technologies, platforms, or data science tools.
+ Strong data visualization and reporting skills to communicate product performance and insights.
+ Experience driving product adoption and managing organizational change.
MINIMUM QUALIFICATIONS:* Education: Bachelor's degree in Business Administration, Computer Science, Healthcare Administration, or a related field
- Experience: 2\+ years of experience in a product, platform, or related program/project management role supporting the end\-to\-end product lifecycle of custom, semi\-custom, and/or vendor\-developed digital solutions
- Knowledge, Skills, and Abilities (Required):
+ Experience working in an agile product development environment.
+ Experience developing product deliverables such as roadmaps, backlogs, process maps, workflow diagrams, and future\-state vision artifacts.
+ Foundational understanding of AI and data science methodologies and concepts, with a strong interest in continuous learning related to artificial intelligence, machine learning, and DevOps/MLOps.
+ Strong analytical, organizational, and stakeholder management skills.
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. Please contact Emory Healthcare’s Human Resources at [email protected]. Please note that one week's advance notice is preferred.
Salary Context
This $101K-$126K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($114K) sits 37% below the category median. Disclosed range: $101K to $126K.
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.
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: $126K - $126K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.