Research Engineer II - ML Ops

Job Description Summary

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

Key Responsibilities

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

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

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?

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 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