Senior Applied AI Systems Engineer

Pearson; Salem (OR); On-site; USD 140,000 - 160,000; Full time

Job summary

Pearson seeks an experienced Lead Specialist, Applied AI Systems Engineer to architect and implement end-to-end AI systems for next-generation K–12 assessment and learning. You will collaborate with AI Science, content development, and software teams to deliver robust, knowledge-grounded AI capabilities within the assessment authoring system.

You will design architectures that evolve with data, models, and evaluation evidence, and ensure responsible AI practices, transparency, and auditable

Qualifications

• Advanced degree in computer science, AI, ML, statistics or related field.
• Experience designing and building complete AI/ML systems end-to-end.
• Strong breadth across predictive ML, retrieval-grounded systems, and agentic architectures.
• Experience evaluating probabilistic systems against ground truth or expert judgment.

Responsibilities

• Design complete modular AI systems spanning data inputs, retrieval, models, agents, tools, and human review.
• Break complex problems into predictive ML, statistical methods, and retrieval-based solutions.
• Build working reference implementations with software engineers for integration.
• Design evolvable architectures as data and models change.
• Develop agentic/workflow systems with state, retrieval, and human decision points.
• Evaluate systems with planning for justice, transparency, and auditability.

Skills

Python

TypeScript

Knowledge graphs

Evaluation

Education

Advanced degree in CS/AI/ML/Statistics

Tools

MLflow

Go

PostgreSQL

Job description

Pearson seeks an experienced Lead Specialist, Applied AI Systems Engineer to architect and implement end-to-end AI systems for next-generation K–12 assessment and learning. You will collaborate with AI Science, content development, and software teams to deliver robust, knowledge-grounded AI capabilities within the assessment authoring system.

You will design architectures that evolve with data, models, and evaluation evidence, and ensure responsible AI practices, transparency, and auditable