Interested in this AI/ML Engineer role at GE HealthCare?
Apply Now →About This Role
Job Description Summary
===========================
Your role is to lead GE HealthCare’s next chapter of AI by scaling enterprise AI capabilities, reusable AI components, and workflow intelligence across products, software, and care pathways. This role focuses on operationalizing AI at enterprise scale — embedding it into regulated environments and enabling hybrid edge/cloud inference across clinical, operational, and enterprise workflows.Job Description
===================
Your Challenge
You will shape where the company places its AI bets across workflow, reporting, operational intelligence, longitudinal care pathways, automation, SaaS, and enterprise software. Success in this role means translating advances in foundation models, multimodal AI, and agentic systems into measurable outcomes — including improved time\-to\-decision, operational workflow performance, and longitudinal intelligence — while accelerating AI\-enabled software and SaaS growth.
Your Responsibilities
- Define and drive GE HealthCare’s enterprise AI strategy, shaping how AI enables product innovation, workflow transformation, enterprise AI architecture, and AI\-enabled software and SaaS growth.
- Scale reusable AI capabilities, enterprise services, and common components that create reusable AI leverage across segments and platforms.
- Lead multidisciplinary teams spanning applied science, AI engineering, enterprise architecture, clinical AI strategy, governance, and workflow orchestration.
- Advance modern enterprise AI architecture, including foundation models, multimodal AI, agentic systems, orchestration layers, and enterprise data layers, enabling seamless integration across products and environments.
- Drive enterprise AI lifecycle management and operationalization at scale, including continuous validation, monitoring, deployment, and performance optimization of AI products across installed base and cloud environments.
- Enable hybrid edge/cloud inference strategies that support real\-time, reliable AI deployment across devices, software, and clinical workflows.
- Partner across business, product, engineering, regulatory, legal, and operational leaders to embed AI into real\-world healthcare environments, improving operational workflow, reporting, and longitudinal care processes.
- Accelerate the transition from standalone AI features to enterprise workflow intelligence that materially improves time\-to\-decision, operational performance, care coordination, and longitudinal intelligence.
- Establish and uphold enterprise\-wide AI governance and Responsible AI frameworks, including regulatory compliance, validation standards, privacy, security, and trust.
- Guide strategic investment decisions to maximize enterprise impact, platform leverage, and differentiated growth.
- Represent GE HealthCare externally with regulators, standards bodies, partners, and academic ecosystems to shape the future of healthcare AI.
Your Experience
- Proven executive leadership in artificial intelligence, machine learning, digital health, enterprise software, or a related domain.
- Demonstrated success scaling enterprise AI capabilities into production\-grade environments, including hybrid edge/cloud inference and real\-world deployment.
- Deep experience across the AI lifecycle, including continuous validation, monitoring, governance, and lifecycle management of AI products across installed base and cloud environments.
- Strong understanding of modern AI systems and enterprise AI architecture, including foundation models, multimodal AI, agentic systems, orchestration, and enterprise data layers.
- Track record of building reusable AI capabilities and shared platforms that drive enterprise leverage across multiple products, segments, or business units.
- Experience translating AI investments into measurable business outcomes, including improvements in time\-to\-decision, operational workflow, and software/SaaS growth.
- Experience operating within regulated environments, partnering across engineering, product, regulatory, legal, and commercial functions.
- Strong external credibility and engagement across industry, partners, and the broader AI ecosystem.
Desired Characteristics
- Enterprise thinker who connects AI strategy to architecture, workflow, and business outcomes.
- Operator\-builder who can scale AI capability and reusable enterprise leverage, not just vision.
- Deeply grounded in real\-world application of AI, including operational workflow integration, monitoring, and lifecycle performance.
- Strong orientation toward Responsible AI, governance, and trust in complex, regulated environments.
- Highly credible across technical, product, and executive audiences.
- Pragmatic innovator who balances cutting\-edge capabilities (e.g., foundation models, multimodal AI, agentic systems) with operational rigor and business impact.
- External thought leader who helps shape the direction of healthcare AI.
For U.S. based positions only, the pay range for this position is $348,000\.00\-$522,000\.00 Annual. It is not typical for an individual to be hired at or near the top of the pay range and compensation decisions are dependent on the facts and circumstances of each case. The specific compensation offered to a candidate may be influenced by a variety of factors including skills, qualifications, experience and location. In addition, this position may also be eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). GE HealthCare offers a competitive benefits package, including not but limited to medical, dental, vision, paid time off, a 401(k) plan with employee and company contribution opportunities, life, disability, and accident insurance, and tuition reimbursement.*This role is restricted to U.S. persons (i.e., U.S. citizens, permanent residents, and other protected individuals under the Immigration and Naturalization Act, 8 U.S.C. 1324b(a)(3\)) due to access to export\-controlled technology. GE HealthCare will require proof of status prior to employment.*
Additional Information
==========================
GE HealthCare offers a great work environment, professional development, challenging careers, and competitive compensation. GE HealthCare is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE HealthCare will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
While GE HealthCare does not currently require U.S. employees to be vaccinated against COVID\-19, some GE HealthCare customers have vaccination mandates that may apply to certain GE HealthCare employees.
Relocation Assistance Provided: Yes
Salary Context
This $348K-$522K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At GE 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 in Demand for This Role
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 $178,940 based on 11,900 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $259,000. This role's midpoint ($435K) sits 143% above the category median. Disclosed range: $348K to $522K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
GE HealthCare AI Hiring
GE HealthCare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $522K - $522K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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.