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About This Role
Job Description Summary
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The AI Scientist will work in teams addressing statistical, machine learning and data understanding problems in a commercial technology and collaborative Are you passionate about applying Generative AI to transform healthcare? GE HealthCare is seeking a Senior Staff AI Engineer to design, build, deploy, and optimize production\-grade AI solutions that power next\-generation clinical applications. In this role, you will develop scalable AI systems using large language models (LLMs), foundation models, and modern machine learning technologies to automate clinical workflows while ensuring reliability, performance, and responsible AI deployment.Job Description
===================
### About the Role
At GE HealthCare, we are bringing AI\- and cloud\-based technologies to healthcare by delivering advanced analytics, visualization, multimodal learning, intelligent software, and scalable computing solutions across cloud and edge environments. Our Science \& Technology organization develops AI capabilities that improve clinical workflows, enhance provider productivity, and help make healthcare more personalized, precise, and accessible.
What You Will Do
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- Design, develop, and deploy production\-ready AI solutions using Large Language Models (LLMs), foundation models, and modern machine learning techniques to automate clinical workflows.
- Build scalable AI applications leveraging electronic medical records (EMRs), medical waveforms, clinical reports, and other healthcare datasets.
- Develop robust inference pipelines, model serving infrastructure, and AI services optimized for reliability, scalability, latency, and cost.
- Optimize foundation models through prompt engineering, fine\-tuning, distillation, quantization, and inference optimization techniques.
- Implement responsible AI practices, including model evaluation, robustness testing, monitoring, and human\-in\-the\-loop feedback mechanisms.
- Collaborate with research scientists and cross\-functional engineering teams to transition advanced AI models into production environments.
- Build reusable software components, APIs, and development frameworks that enable scalable AI application development.
- Stay current with emerging AI technologies, open\-source frameworks, and industry best practices to continuously improve GE HealthCare's AI platform.
Required Qualifications
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### Education
- Master's degree in Science, Technology, Engineering, Mathematics (STEM), Computer Science, Artificial Intelligence, or a related technical field with 3\+ years of relevant experience, or
- PhD in a STEM discipline with relevant experience developing production AI systems.
### AI \& Machine Learning
- Demonstrated experience building and deploying large\-scale Generative AI or foundation model solutions.
- Experience developing applications using Large Language Models (LLMs), Agentic AI, or self\-supervised learning techniques.
- Strong understanding of modern machine learning techniques including transfer learning, generative models, optimization, and model evaluation.
### Software Engineering
- Strong programming skills in Python and C\+\+.
- Experience developing scalable, maintainable, production\-quality software.
- Experience designing APIs, distributed services, or cloud\-native AI applications.
### AI Infrastructure
- Experience with modern AI frameworks such as PyTorch, Hugging Face, DeepSpeed, Megatron, or PyTorch Lightning.
- Experience deploying AI workloads using MLOps, ModelOps, or Foundation Model Operations (FMOps) practices.
- Experience working with large\-scale model training or inference infrastructure.
### Healthcare Data
- Experience working with high\-dimensional medical imaging, waveform, or time\-series clinical datasets.
Preferred Qualifications
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- Experience solving complex engineering problems with ambiguous requirements.
- Experience deploying large\-scale distributed AI systems.
- Experience with Spark, Hadoop, TensorFlow, or PyTorch in enterprise environments.
- Experience building production data platforms and AI\-powered software applications.
- Track record of delivering machine learning solutions using large real\-world healthcare datasets.
- Experience optimizing large\-scale AI training and inference performance.
Why Join GE HealthCare
==========================
Join a global AI engineering organization building production AI systems that improve patient care at scale. You'll work with world\-class engineers and scientists across the United States, France, Israel, and India to bring cutting\-edge Generative AI capabilities into healthcare products that make a meaningful difference for clinicians and patients worldwide.
Work Environment
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### Global Collaboration
Our AI teams are located across Bellevue, San Ramon, France, Israel (Tel Aviv), and India (Bangalore), providing opportunities to collaborate across a diverse, global engineering organization.
### Work\-Life Balance
We value flexibility and recognize that maintaining a healthy work\-life balance enables our teams to perform at their best. Our flexible work environment supports both professional success and personal well\-being.
### Career Growth
You'll collaborate with experienced engineers, researchers, and technical leaders while benefiting from mentorship opportunities across GE HealthCare's global engineering organization.
We will not sponsor individuals for employment visas, now or in the future, for this job opening. For U.S. based positions only, the pay range for this position is $160,080\.00\-$240,120\.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.
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 $160K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $160K to $240K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
GE HealthCare AI Hiring
GE HealthCare has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $240K - $282K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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