Lead AI Scientist

Pearson VUE

. Design complete, modular AI systems spanning knowledge and data inputs, retrieval, models, agents, tools, deterministic services, human review, evaluation, and integration .

Posted 11/4/2026full-timeRemote • Texas • United StatesSenior💰 $140,000 - $160,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building complete AI systems, including predictive ML, statistical methods, and knowledge-grounded architectures. Proficient in collaborating with cross-functional teams to translate domain expertise into measurable evaluation criteria and ensuring responsible AI practices.

Highest-signal resume keywords

Advanced Degree In Computer ScienceStrong Python ProficiencyExperience Designing AI Systems End To EndKnowledge Graphs And Knowledge-Grounded AIExperience With Parameter-Efficient Fine-Tuning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive MLStatistical LearningLLM-Based SystemsError AnalysisRegression TestingModel Adaptation MethodsSynthetic Data DesignGraph-Grounded AgentsHybrid RetrievalCalibration And Drift Detection

Soft Skills

CollaborationTechnical CommunicationInnovative Problem SolvingJudgment And Autonomy

Tools & Technologies

TypeScriptAWSPostgreSQLGraph Data StoresMLflow

Industry Keywords

Educational MeasurementPsychometricsLearning ScienceEvaluation DatasetsExpert-Adjudicated Datasets

Tech Stack

Tools & technologies

AWSPostgresPythonTypeScriptGo

About the role

Key responsibilities & impact

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 appropriate combinations 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 integrate them into Pearson’s assessment authoring system
Design architectures that evolve as models, data, domain knowledge, and evaluation evidence change
Design and implement agentic and multi-step workflows using models, tools, retrieval, state, structured outputs, and human decision points
Build knowledge-grounded systems using graph, semantic, vector, and hybrid retrieval
Co-design knowledge and graph architecture with the AI Data & Knowledge Architect
Develop AI capabilities for content creation and revision, verification, framework alignment, guidance, and analytic insights
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
Monitor deployed systems for drift and emerging failures and use findings to drive improvement
Develop predictive models, classifiers, and ranking methods where they outperform generative AI
Evaluate prompting, retrieval, structured knowledge, model selection, and model adaptation tradeoffs
Apply parameter-efficient fine-tuning and other adaptation methods when supported by evaluation evidence
Develop versioned evaluation and training assets from expert judgments and adjudicated examples
Design synthetic-data approaches with rigorous quality controls
Prototype quickly, test assumptions empirically, and pivot when evidence warrants
Build accurate, transparent, auditable AI that supports rather than supplants human expertise
Uphold responsible AI and data practices, including privacy, security, fairness, and appropriate use of student and content data
Collaborate with Technology teams, software engineers, AI Science colleagues, the AI Data & Knowledge Architect, psychometricians, learning scientists, and content development teams
Communicate findings, tradeoffs, and limitations to technical and non-technical audiences

Requirements

What you’ll need

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
Experience with TypeScript or 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: Experience building knowledge and inference layers of education, learning, or assessment systems
Preferred: Experience building AI or ML systems in evidence-sensitive domains
Preferred: Hands-on experience with parameter-efficient fine-tuning or other model-adaptation methods
Preferred: Experience designing, filtering, and validating synthetic data
Preferred: Experience building expert-annotated datasets, adjudication protocols, or evaluation platforms
Preferred: Experience with graph-grounded agents, hybrid retrieval, or knowledge-intensive AI applications
Preferred: Familiarity with ontologies, semantic models, or hybrid neural-symbolic architectures
Preferred: Experience with calibration, uncertainty estimation, drift detection, or model monitoring
Preferred: Experience with TypeScript, AWS, PostgreSQL, graph data stores, Go, Python, and ML lifecycle tools such as MLflow
Preferred: Familiarity with educational measurement, psychometrics, or learning science

Benefits

Comp & perks

Annual incentive program eligibility
Benefits information provided by Pearson