Scientist Jobs

About The Position

Requirements

• 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 machine learning, statistical learning, and LLM-based, agentic, and retrieval-grounded systems, with judgment about when each is appropriate.
• 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 innovation beyond established practice, sound technical judgment, and high autonomy.
• Ability to collaborate closely with engineers and domain experts, translating expertise into requirements and evaluation criteria.

Nice To Haves

• Experience building 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 inference behind learner insight reporting.
• Experience building AI or machine-learning 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; the environment also uses Go, Python, and ML lifecycle tools such as MLflow.
• Familiarity with educational measurement, psychometrics, or learning science.

Responsibilities

• Design modular end-to-end 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 machine learning, 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 the assessment authoring system.
• Design architectures that can 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, or hybrid retrieval as appropriate.
• Co-design knowledge and graph architecture with the AI Data & Knowledge Architect and contribute substantively to the knowledge layer.
• 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.
• Work with psychometricians and content experts to translate domain expectations into measurable evaluation 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.
• Develop predictive models, classifiers, and ranking methods when 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 while keeping training data separate from protected evaluation data.
• Design synthetic-data approaches with rigorous quality controls and avoid treating generated data as ground truth by default.
• Prototype quickly, test assumptions empirically, and pivot when evidence warrants.
• Build accurate, transparent, auditable AI 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.
• 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.

Benefits

• Eligible to participate in an annual incentive program.
• Benefits information is available at Pearson Benefits Global.