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# Senior ML Platform Engineer - Artificial...

- **Company:** Bloomberg L.P.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $160,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Engineering, Continuous Integration, Data Structures, Python (Programming Language), Object-Oriented Software Development, Open Source Technology, Azure Machine Learning, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations
- **Published:** June 12, 2026
- **Apply:** https://www.juju.com/job/00000000g7eodb

## About the Role

+ 4+ years of experience working with an object-oriented programming language (Python, Go, etc.)

 + Experience designing cloud-native, distributed platforms

 + Strong knowledge of Kubernetes, Argo, and container orchestration technologies

 + Previous experience with modern CI/CD tools and GitOps workflows

 + Familiarity with implementing automation for model development lifecycles

 + A proactive mentality and ability to collaborate with peers, stakeholders, and management

 + A Degree in Computer Science, Engineering, Mathematics, or similar field of study or equivalent work experience

 + An understanding of Computer Science fundamentals such as data structures and algorithms

## Description

As a Senior ML Platform Engineer, you will have the opportunity to create a more cohesive, integrated, and managed AI development life cycle to enable the building and maintenance of our AI systems. Our teams make extensive use of open source technologies such as, Kubernetes, Kubeflow, KServe, Argo, Buildpacks, and other cloud-native MLOps technologies. From technical governance to upstream collaboration, we are committed to enhancing the impact and sustainability of open source.

Join the AI Group as a Senior ML Platform Engineer and you will have the opportunity to:

 + Architect, build, and diagnose multi-tenant AI platform systems

 + Work closely with AI application teams to design seamless workflows for continuous model training, inference, and monitoring

 + Work with AI experts to understand workflows, pinpoint, and resolve inefficiencies, and to inform the next set of features for the platforms

 + Collaborate with open-source communities and AI application teams to build a cohesive MLOps experience

 + Design CI/CD automation frameworks that incorporate regulatory requirements


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