About this role
Pearson seeks a Lead Specialist, AI Scientist to build and scale production AI/ML capabilities for learner intelligence, knowledge graphs, recommendations, personalized learning and AI-powered products. The role spans research, data science, software engineering and product delivery, with responsibility for moving solutions from experimentation into reliable production use.
Responsibilities
- Lead the design, development, deployment and operation of machine learning, generative AI, LLM, retrieval-augmented generation and agentic AI solutions. Build reusable AI services, APIs, workflows and platform components for Pearson products.
- Own the AI delivery lifecycle, including prototyping, monitoring, evaluation and continuous improvement. Establish MLOps and AIOps practices covering training, deployment, observability, governance and reliability.
- Evaluate foundation models and architectures against quality, safety, scalability, latency and cost. Set practices for responsible AI, prompt engineering, model and agent evaluation, and AI governance.
- Partner with Product, Engineering, Design, Learning Science and Data Science teams; mentor engineers and data scientists; and communicate technical decisions, risks and outcomes to stakeholders. Expected results include production-ready personalization and recommendation capabilities, enterprise-scale AI applications, and measurable learner and business impact.
Required qualifications
- 5+ years building and deploying production AI/ML systems, including cloud-native applications and MLOps. Experience with applied machine learning and relevant approaches such as generative AI, LLMs, RAG, recommendation systems, knowledge graphs or agentic AI.
- Hands-on foundation-model application development; Python, APIs, testing, CI/CD, version control and production operations; scalable AI architectures in AWS or similar cloud environments; containerization, orchestration and infrastructure as code. Experience evaluating and optimizing AI systems, plus strong stakeholder collaboration and communication.
- Bachelor's degree in Computer Science, Engineering, Data Science or AI/ML, or equivalent practical experience.
Preferred
A master's degree or PhD in a related field; educational technology or learning-science experience; familiarity with psychometrics, proficiency modeling, Bayesian methods, item response theory or educational measurement; enterprise-scale AI platform, knowledge graph or agentic-system work; or research, patent, open-source or industry contributions.
The role description specifies Remote, United States, while the listing header labels Richmond, VA and Hybrid. The full-time salary range is $150,000–$190,000, with eligibility for an annual incentive program. The posting says applications will be accepted through August 30th, subject to possible extension.