Lead Specialist, AI Scientist

What matters most

• Production AI for personalized learning

  Lead deployed learner-intelligence, recommendation, knowledge-graph and AI-powered learning capabilities. The posting emphasizes measurable learner engagement, efficacy and business outcomes, not just working prototypes.

• Generative AI delivery

  Design and deliver LLM, retrieval-augmented generation and agentic AI solutions that integrate into Pearson products and create measurable impact.

• Reusable platforms and operational ownership

  Build shared AI services, APIs and workflows, and own the path from experimentation through deployment, monitoring, evaluation and improvement. Establish scalable MLOps and AIOps practices.

• Evaluation and responsible operation

  Assess models and architectures against quality, safety, scalability, latency and cost. Establish practices for model and agent evaluation, responsible AI and governance.

• Technical leadership across disciplines

  Partner with Product, Engineering, Design, Learning Science and Data Science; mentor technical colleagues; and explain architecture decisions, risks and outcomes to stakeholders.

Likely challenges to think through

• Measuring learning impact

  Likely preparation challenge: a useful AI feature may not demonstrate improved learning efficacy. Prepare to explain how you would distinguish engagement or productivity measures from learning outcomes and work with Learning Science on evaluation; the posting does not prescribe specific metrics.

• Balancing model performance and operating constraints

  Likely preparation challenge: higher-quality LLM or agentic outputs may increase latency, cost or safety risk. Prepare a concrete approach to comparing architectures, setting evaluation criteria and making an explicit deployment trade-off.

• Turning experiments into reliable services

  Likely preparation challenge: rapid prototyping can conflict with the testing, observability, governance and reliability needed in production. Prepare to walk through how you would introduce those controls while preserving a route to iteration.

• Sharing capabilities across products

  Likely preparation challenge: reusable platform components must serve different product needs without becoming too rigid or too bespoke. Prepare to describe how you would choose common APIs and workflows while coordinating priorities with product and engineering partners.

Topics to prepare

• LLM, RAG and agent architecture

  The posting calls for generative AI, retrieval-augmented generation and agentic solutions. As a revision exercise, compare when a learner-facing task would use retrieval, an agentic workflow or neither; explain the quality, safety, latency and cost trade-offs.

• Recommendations and knowledge graphs

  Pearson names learner intelligence, recommendations, knowledge graphs and personalized learning as production capabilities. Sketch how relationships between learning content and learner needs might inform a recommendation, then identify what evidence would show it improves learning outcomes.

• Production AI lifecycle

  The role owns experimentation through deployment, monitoring, evaluation and improvement, with MLOps and AIOps practices. Trace a hypothetical model change through those stages, noting versioning, observability, reliability and rollback decisions.

• Evaluation and responsible AI

  The posting explicitly covers model and agent evaluation, prompt engineering, governance, and optimization for quality, safety, scalability, latency and cost. Draft an evaluation plan with failure cases and thresholds, and explain which trade-offs require stakeholder decisions.

• Learning-science measurement

  Learning science is a collaboration area; psychometrics, proficiency modeling, Bayesian methods and item response theory are preferred familiarity, not stated requirements. As optional preparation, contrast engagement with learning efficacy and outline how an assessment signal might inform—but not alone prove—a personalization benefit.

Skills to revise

• Build and test a Python AI API

  Python, APIs, testing, CI/CD and version control are explicit qualifications. Practice implementing a small AI-backed endpoint with input checks and automated tests, then describe how a versioned change would move through CI/CD.

• Prototype a retrieval or agent workflow

  The role calls for hands-on foundation-model applications and reusable workflows. As a practice exercise, build a small RAG or agentic prototype, record its failure cases, and explain what would need to change before it could serve a Pearson product; no particular listed framework is mandatory.

• Plan a cloud-native deployment

  The posting specifies AWS or a similar cloud, containerization, orchestration and infrastructure as code. Prepare a deployment design for the prototype showing service boundaries, configuration, scaling and recovery; distinguish the design exercise from any claim about Pearson's actual architecture.

• Run an evaluation and monitoring pass

  Production monitoring and optimization are explicit duties. Test a small set of representative and adverse inputs, record quality and safety outcomes alongside latency and cost, and propose alerts and a response to degraded results.

• Explain technical trade-offs across teams

  The role partners with Product, Engineering, Design, Learning Science and Data Science, mentors colleagues, and communicates decisions to stakeholders. Rehearse a brief recommendation between two AI approaches that states expected learner outcome, operational risks, evidence gaps and the decision needed.

• Map evidence to the required qualifications

  Prepare a one-page inventory of examples covering production AI/ML, cloud-native deployment, MLOps, Python, APIs, testing, CI/CD, containers, orchestration and infrastructure as code. The posting requires these; mark any area without a direct example rather than inventing one.

• Build a production project walkthrough

  Choose one relevant project, if available, and prepare a concise account of the problem, your decisions, deployment path, monitoring, changes after launch and measured outcomes. Pearson emphasizes taking AI capabilities from experimentation to production impact.

• Practice a personalized-learning design exercise

  As preparation, sketch an architecture for learner recommendations or a knowledge-graph-supported learning experience. Show data flow, reusable services and APIs, deployment approach, and how you would measure learner engagement or efficacy; this is a practice exercise, not a stated interview task.

• Prepare an AI evaluation and operations plan

  Draft a sample plan for a RAG or agentic application comparing foundation-model options on quality, safety, latency, scalability and cost. Include evaluation criteria, observability, governance and a response to performance regressions—areas named in the role.

• Rehearse a cross-functional leadership example

  Prepare an example, if available, of working across product and technical teams or mentoring colleagues. Be ready to explain a strategy, trade-off, risk and outcome to both technical and nontechnical stakeholders.

• Review relevant learning-domain concepts

  If applicable, prepare a brief example involving personalized learning, assessment or learning science. Psychometrics, proficiency modeling, Bayesian methods and item response theory are preferred familiarity, not stated requirements.

• Write focused questions for Pearson

  Prepare questions about how the team measures learner outcomes, prioritizes reusable AI platform work, and divides responsibility for evaluation and operations. Ask which work arrangement applies to this role: the description says remote in the United States, while the listing says Atlanta and Hybrid.