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Job Title: Specialist, Integration and Test Engineer

Pearson

Lead Specialist, AI Scientist

Remote, United States

The full-time salary range for this role is $140,000 – $160,000.

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Job details

Pay: The full-time salary range for this role is $140,000 – $160,000.
Apply by: Nov 20, 2026
Posting language: English
Posted: Nov 3, 2026

Original job description

Role Overview We are seeking a Lead Specialist, Applied AI Systems Engineer, to design, build, and continuously improve the intelligent systems that support Pearson’s work developing the next generation of K–12 assessment and learning. This role sits within the Measurement & Learning team and operates at the intersection of Content Development, Learning Science, Technology, AI Science, and Psychometrics, partnering closely with software engineers and the AI Data & Knowledge Architect to turn assessment and measurement expertise into testable AI capabilities within our assessment authoring system, which supports formative and summative assessment for Pearson and its partners. This hands-on role joins in the early phases of this work, improving and extending complete AI systems built on a shared knowledge foundation, rather than focusing on prompts or chatbots. The work spans predictive machine learning, statistical methods, retrieval, graph-based approaches, generative and agentic AI, and deterministic software, chosen for reliability rather than novelty. We are particularly interested in innovators who have built AI or machine-learning systems whose outputs must be rigorously evaluated against ground truth, expert judgment, or reliable evidence, such as scientific, healthcare, financial, cybersecurity, legal, or assessment applications. Candidates whose experience is mainly in conversational or customer-service AI should also show substantial experience evaluating systems against defined performance standards. Experience in education, learning, or assessment is strongly preferred when it involves the systems beneath products, such as domain and skill models, learner models, standards and competency frameworks as structured data, or adaptive and recommendation engines, rather than standalone tutoring, chatbot, or content-generation applications. End-to-End AI Systems Design

Design complete, modular AI systems spanning knowledge and data inputs, retrieval, models, agents, tools, deterministic services, human review, evaluation, and integration
Break complex problems into the right combination of predictive ML, statistical methods, generative models, retrieval, graph reasoning, deterministic software, and human decision points
Build working systems and reference implementations, and partner with software engineers to bring them into the assessment authoring system
Design architectures that can evolve as models, data, domain knowledge, and evaluation evidence change

Agentic & Knowledge-Grounded AI

Design and implement agentic and multi-step workflows using models, tools, retrieval, state, structured outputs, and human decision points
Build knowledge-grounded systems that use graph, semantic, vector, and hybrid retrieval as appropriate
Co-design knowledge and graph architecture with the AI Data & Knowledge Architect, contributing substantively rather than treating the knowledge layer as a black box
Develop AI capabilities for content creation and revision, verification, alignment across frameworks, guidance, and analytic insights

Evaluation-Driven Development

Make evaluation part of how systems are built, not a final step
Build and maintain evaluation benches, including expert-adjudicated datasets, challenge sets, regression suites, and protected holdout data
Partner with psychometricians and content experts to turn domain expectations into measurable criteria
Analyze failure modes, critical errors, and performance across meaningful data and task slices, not only aggregate metrics
Monitor deployed systems for drift and emerging failures, and use findings to drive improvement

Machine Learning, Model Adaptation & Data

Develop predictive models, classifiers, and ranking methods where they outperform generative AI
Evaluate tradeoffs among prompting, retrieval, structured knowledge, model selection, and model adaptation, without locking the architecture to a single provider
Apply parameter-efficient fine-tuning and other adaptation methods when evaluation evidence shows they add value
Turn expert judgments and adjudicated examples into versioned evaluation and training assets, keeping training data strictly separate from protected evaluation data
Design synthetic-data approaches with rigorous quality controls, never treating generated data as ground truth by default

Innovation, Judgment & Responsible AI

Work with a high degree of autonomy on problems that have no established solutions
Prototype quickly, test assumptions empirically, and pivot when evidence warrants
Build AI that is accurate, transparent in its reasoning, auditable, and designed to support rather than supplant human expertise
Uphold responsible AI and data practices, including privacy, security, fairness, and appropriate use of student and content data

Cross-Functional Collaboration

Partner with Technology teams and software engineers to integrate AI capabilities into the assessment authoring system
Collaborate with AI Science colleagues and the AI Data & Knowledge Architect on shared architecture and methods
Work with psychometricians and learning scientists to translate measurement and learning requirements into evaluation criteria
Partner with content development teams to understand workflows, quality requirements, edge cases, and human-review needs
Communicate findings, tradeoffs, and limitations clearly to technical and non-technical audiences

Required Qualifications

Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related field, or equivalent experience
Experience designing and building complete AI or machine-learning systems end to end, not only isolated models or prompts
Strong breadth across predictive ML, statistical learning, and LLM-based, agentic, and retrieval-grounded systems, with judgment about when each fits
Demonstrated experience evaluating probabilistic systems against ground truth or expert judgment, including evaluation datasets, error analysis, and regression testing
Experience with knowledge graphs and knowledge-grounded AI sufficient to contribute to architecture decisions
Strong Python proficiency, and experience with TypeScript or the ability to become productive quickly in a TypeScript codebase
Track record of innovating beyond established practice, with sound technical judgment and high autonomy
Ability to collaborate closely with engineers and domain experts, translating expertise into requirements and evaluation criteria

Preferred Qualifications

Experience building the knowledge and inference layers of education, learning, or assessment systems, such as skill or competency graphs, learner or domain models, standards alignment engines, adaptive or recommendation logic, or the inference behind learner insight reporting
Experience building AI or ML systems in evidence-sensitive domains such as scientific, healthcare, financial, legal, or assessment applications
Hands-on experience with parameter-efficient fine-tuning or other model-adaptation methods
Experience designing, filtering, and validating synthetic data for evaluation or model improvement
Experience building expert-annotated datasets, adjudication protocols, or evaluation platforms
Experience with graph-grounded agents, hybrid retrieval, or other knowledge-intensive AI applications
Familiarity with ontologies, semantic models, or hybrid neural-symbolic architectures
Experience with calibration, uncertainty estimation, drift detection, or model monitoring
Experience with TypeScript, AWS, PostgreSQL, and graph data stores; our environment also uses Go, Python, and ML lifecycle tools such as MLflow
Familiarity with educational measurement, psychometrics, or learning science

Design Philosophy

Complete systems over isolated models
Evaluation as part of engineering, not an afterthought
Innovation at the edge of current practice, grounded in evidence
Transparent, auditable, and accurate AI
AI that supports rather than supplants human expertise
The right method for the problem, not the newest one

Why This Role Matters This role will build the intelligent core of an industry-defining assessment system. The goal is not convincing AI output but systems whose quality can be measured, explained, and continuously improved. By inventing new approaches and proving they work, this engineer will help define what intelligent formative and summative assessment can become. Compensation at Pearson is influenced by factors including skill set, experience, and location. The full-time salary range for this role is $140,000 – $160,000. This position is eligible to participate in an annual incentive program. Information on benefits can be found here. Applications will be accepted through 20th November 2026. This window may be extended depending on business needs. Apply today and imagine the impact you can make.

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Skills

Machine learning
Cybersecurity
Tutoring
Chatbot
ML
Content creation
Collaboration
Computer science
Artificial intelligence
Statistics
Python
TypeScript
Grading systems
Reporting
Estimating
AWS
PostgreSQL
Go
Mlflow

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