Job Description

Senior Machine Learning Platform Engineer

About Pearson

As the world's learning company, Pearson helps people make more of their lives through learning. We use our knowledge, passion, and reach to tackle some of the biggest challenges in education and inspire a love of learning that lasts a lifetime. Together, we transform education and provide meaningful opportunities for millions of learners worldwide.

The Automated Assessment team develops machine learning-based software systems that evaluate tens of millions of learner responses each year. Our technology combines large-scale distributed systems, cloud computing, natural language processing, and machine learning to deliver fast, reliable scoring that supports educators, students, and parents around the world.

As advances in AI continue to reshape education, our team is building the next generation of machine learning infrastructure that powers both traditional scoring models and emerging generative AI capabilities.

The Opportunity

We are looking for a Senior Machine Learning Platform Engineer to lead the evolution of our cloud-native machine learning platform. This role is responsible for designing and developing the infrastructure that enables data scientists and machine learning engineers to efficiently build, train, deploy, and operate production machine learning models at scale.

You will help define the future of our AI platform, including distributed model training, GPU-based workloads, large language model hosting, and the tooling that enables research to become reliable production systems.

This position offers the opportunity to influence architectural direction while working closely with software engineers, AI scientists, and product teams on technology that directly impacts millions of learners.

Responsibilities

As a Senior Machine Learning Platform Engineer, you will:

• 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 (e.g. transformer-based classifiers), 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.

Required Qualifications

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

Preferred Qualifications

Experience with one or more of the following:

• Machine learning platforms such as MetaFlow, MLflow, Kubeflow, or similar workflow orchestration systems.
• Production machine learning systems.
• GPU computing and distributed model training.
• Large language model deployment or inference infrastructure.
• PyTorch, TensorFlow, or similar machine learning frameworks.
• Kubernetes operations, scheduling, and workload optimization.
• Go development.
• Infrastructure as Code technologies.
• Performance optimization and cloud cost management.
• Building internal developer platforms or engineering productivity tools.

What Will Set You Apart

• Experience building platforms used by machine learning engineers and data scientists.
• Experience deploying and operating production AI or LLM infrastructure.
• Experience fine-tuning/deploying/managing foundation models and pipelines.
• Experience designing highly scalable cloud-native systems handling large datasets and compute-intensive workloads.
• Curiosity about emerging AI technologies and the ability to evaluate them pragmatically.
• A passion for building tools that enable others to move faster.

Why Join Pearson?

You'll help build the platform that powers AI across Pearson's automated assessment ecosystem. Your work will enable machine learning scientists to innovate faster while ensuring our production systems remain scalable, secure, reliable, and cost-effective.

This is an opportunity to work on challenging engineering problems at the intersection of distributed systems, cloud infrastructure, machine learning, and generative AI—developing technology that directly improves educational outcomes for learners around the world.

Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. As required by the California, Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, New York State, New York City, Vermont, Washington State, and Washington DC laws, the pay range for this position is as follows:

The minimum full-time salary range is between $135,000 - $155,000.

This position is not bonus eligible, and information on benefits offered is here.

Applications will be accepted through 21st September. This window may be extended depending on business needs.

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