Senior ML Platform Engineer: Cloud-Native AI Infra

Pearson; City of Albany (NY); On-site; USD 135,000 - 155,000; Full time

Job summary

Pearson's Automated Assessment team is building a cloud-native machine learning platform to enable data scientists and ML engineers to train, deploy, and operate models at scale. The Senior ML Platform Engineer will lead infrastructure evolution and platform design across large-scale training and deployment.

You will collaborate with AI scientists and software engineers to deliver production-ready systems, scalable hosting for LLMs, and tooling for research-to-production transitions.

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• Strong software engineering experience with distributed systems.
• Experience designing and building cloud-native applications on AWS.
• Experience with Kubernetes and container technologies.
• Experience designing REST-based APIs and microservice architectures.
• Experience with SQL and NoSQL databases.
• Experience with CI/CD, Git workflows, and automated testing.
• Strong problem-solving, communication, and collaboration skills.

Responsibilities

• Lead the design and evolution of Pearson's Kubernetes-based machine learning platform supporting large-scale model training and deployment.
• Design, implement, and optimize distributed machine learning workflows using MetaFlow and other cloud-native technologies.
• Build platform capabilities that enable reproducible experimentation, automated model training, artifact management, and production deployment.
• Develop infrastructure supporting GPU-based machine learning workloads for traditional ML models, foundational models, and agentic pipelines.
• Design and implement backend services and APIs that support machine learning lifecycle management.
• Evaluate and integrate open-source technologies that improve developer productivity, platform reliability, scalability, and operational efficiency.
• Collaborate closely with AI scientists to transition research prototypes into robust, scalable, production-quality systems.
• Improve platform observability, reliability, security, and cloud cost efficiency.
• Mentor engineers, contribute to technical strategy, and help establish engineering best practices across the team.

Skills

Distributed systems

AWS cloud

Kubernetes

REST APIs

SQL NoSQL

CI/CD

Git workflows

Automated testing

Communication

Education

CS/SE degree or related

Tools

Kubernetes

Docker

MetaFlow

Kubeflow

Terraform

Job description

Pearson's Automated Assessment team is building a cloud-native machine learning platform to enable data scientists and ML engineers to train, deploy, and operate models at scale. The Senior ML Platform Engineer will lead infrastructure evolution and platform design across large-scale training and deployment.

You will collaborate with AI scientists and software engineers to deliver production-ready systems, scalable hosting for LLMs, and tooling for research-to-production transitions.