Senior AI Systems Engineer — Knowledge-Grounded & Evaluation

Pearson; Indianapolis (IN); On-site; USD 140,000 - 160,000; Full time

Job summary

Pearson is seeking a Lead Specialist, Applied AI Systems Engineer to design and build intelligent AI systems for K–12 assessment and learning. This hands-on role spans knowledge graphs, retrieval, and agentic AI, with close collaboration to integrate into Pearson’s assessment authoring system and evidence-driven evaluation.

The role emphasizes design of robust, transparent AI that can be evolved with data, domain knowledge, and evaluation outcomes, ensuring reliability and alignment with

Qualifications

• Advanced degree in Computer Science, AI, Machine Learning, Statistics, or related field, or equivalent experience.
• Experience designing and building complete AI or ML systems end to end, not just isolated models or prompts.
• Strong breadth across predictive ML, statistical learning, and retrieval-grounded systems with good judgment.
• Proven experience evaluating probabilistic systems against ground truth or expert judgment.

Responsibilities

• Design modular AI systems spanning knowledge, data inputs, retrieval, models, agents, tools, deterministic services, human review, evaluation, and integration.
• Build working systems and reference implementations and partner with engineers to integrate into the assessment authoring system.
• Design architectures that evolve with model/data domain knowledge and evaluation evidence.

Skills

Python

TypeScript

Knowledge graphs

Retrieval-augmented AI

Evaluation & benchmarking

Collaboration with engineers

Education

Advanced degree in CS/AI/Statistics or equivalent experience

Tools

ML frameworks

Graph databases

MLflow

Job description

Pearson is seeking a Lead Specialist, Applied AI Systems Engineer to design and build intelligent AI systems for K–12 assessment and learning. This hands-on role spans knowledge graphs, retrieval, and agentic AI, with close collaboration to integrate into Pearson’s assessment authoring system and evidence-driven evaluation.

The role emphasizes design of robust, transparent AI that can be evolved with data, domain knowledge, and evaluation outcomes, ensuring reliability and alignment with