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About This Role
Job Description Summary
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Are you passionate about building reliable cloud infrastructure that helps researchers innovate faster? In this role, you will work at the intersection of cloud engineering, machine learning operations, and research computing, helping teams develop, test, and deploy cutting\-edge solutions that advance MIM Research initiatives.
You'll collaborate with researchers, engineers, and infrastructure partners to create scalable AWS\-based environments, develop internal tools, and build engineering solutions that enable impactful research. We are looking for someone who enjoys solving complex problems, learning new technologies, and working in a collaborative, mission\-driven environment.Job Description
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Key Responsibilities
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In this role, you will:
- Partner with DevOps and infrastructure teams to migrate, optimize, and support research workloads on AWS cloud platforms.
- Design, build, and maintain machine learning operations (MLOps) pipelines that support model training, evaluation, deployment, and monitoring.
- Develop prototypes and internal tools that accelerate experimentation, model development, and research workflows.
- Translate research objectives into scalable, maintainable, and well\-documented engineering solutions.
- Promote and support engineering best practices, including:
+ Code quality, testing, and reliability
+ Documentation and version control
+ Data management and governance
+ Experiment tracking and reproducibility
- Effectively manage multiple projects while balancing fast\-paced research needs with long\-term engineering sustainability.
- Provide technical guidance and mentorship to early\-career engineers and support knowledge sharing across teams.
- Collaborate closely with research scientists, product teams, and infrastructure partners to deliver impactful solutions.
Required Qualifications
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### Education \& Experience
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 2 to 4 years of experience in DevOps, Site Reliability Engineering (SRE), cloud infrastructure, or related technical roles.
- Experience supporting production systems within Amazon Web Services (AWS).
### Cloud \& Infrastructure
- Experience working with AWS services such as:
+ Compute: EC2, ECS, EKS, Lambda
+ Storage: S3, EFS
+ Data: DynamoDB
+ Machine Learning: SageMaker (training, pipelines, and deployment)
- Experience with Infrastructure as Code (IaC) tools such as Ansible, Terraform, CloudFormation, or AWS CDK.
- Familiarity with containerization and orchestration technologies such as Docker, Docker Compose, and Kubernetes.
### DevOps \& Data Engineering
- Experience building and maintaining continuous integration and continuous deployment (CI/CD) systems, including tools such as GitHub Actions, GitLab CI, or Jenkins.
- Strong foundation in Linux systems administration.
- Experience with monitoring and observability practices using tools such as Prometheus, Datadog, or similar technologies.
### Programming \& Software Engineering
- Understanding of software engineering best practices, including:
+ Software design patterns
+ API development (REST and gRPC)
+ Testing methodologies and maintainable code architecture
### Research \& Applied Machine Learning
- Experience supporting research environments or collaborating closely with research teams.
- Ability to work effectively with evolving requirements, experimentation, and iterative development processes.
### Collaboration \& Leadership
- Ability to lead technical initiatives involving multiple stakeholders and cross\-functional teams.
- Experience mentoring engineers and supporting the adoption of engineering best practices.
- Strong communication skills with the ability to connect technical concepts across research and engineering audiences.
Preferred Qualifications
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While not required, the following experiences would be valuable:
- Experience with large\-scale distributed computing frameworks such as Spark or Ray.
- Background in high\-performance computing (HPC) or research computing environments.
- Familiarity with data governance, compliance requirements, or regulated industries.
- Contributions to open\-source projects or published research.
- Relevant certifications such as:
+ RHCSA or RHCE
+ CKAD
+ AWS Certified Solutions Architect – Associate
+ Or equivalent hands\-on experience
What Success Looks Like
===========================
In this role, you will help create an environment where:
- Research teams can efficiently train, evaluate, and deploy machine learning models.
- Reliable and scalable infrastructure enables research teams to innovate with confidence.
- Best practices for reproducibility, testing, governance, and documentation are consistently adopted.
- Engineers at all levels receive mentorship and opportunities to grow.
- Projects are delivered effectively and aligned with organizational priorities.
- Research and engineering teams work together seamlessly to accelerate meaningful outcomes.
Why Join Us?
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- Help build technology that empowers researchers and drives innovation.
- Work alongside collaborative teams of researchers, engineers, and technical leaders.
- Contribute to meaningful projects with real\-world impact.
- Grow your technical expertise across cloud infrastructure, machine learning operations, and research computing.
- Share knowledge, mentor others, and continue developing your leadership skills in a supportive environment.
- Be part of a culture that values diverse perspectives, continuous learning, and inclusive collaboration.
\#LI\-CC1
We will not sponsor individuals for employment visas, now or in the future, for this job opening.
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
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At GE HealthCare, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $220,000 based on 47 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
GE HealthCare AI Hiring
GE HealthCare has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Cleveland, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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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