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 taking solutions from experimentation into reliable production use.
Responsibilities
- Lead the design, deployment and operation of machine learning, generative AI, LLM, retrieval-augmented generation and agentic AI solutions. Build reusable platform services, APIs and workflows for use across Pearson products.
- Own the AI delivery lifecycle, including prototyping, deployment, monitoring, evaluation and continuous improvement. Establish MLOps and AIOps practices for observability, governance, reliability and operational excellence.
- 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 technical colleagues and communicate strategy, trade-offs, risks and results. Deliver production-ready learner intelligence and recommendation capabilities, enterprise-scale AI applications, and measurable learner and business outcomes.
Required qualifications
- 5+ years building and deploying production AI/ML systems, including cloud-native applications and MLOps practices. Experience with applied machine learning and technologies 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. Experience with scalable AI platforms and deployment architectures in AWS or similar cloud environments, plus containerization, orchestration and infrastructure as code.
- Experience evaluating and optimizing AI systems for quality, reliability, safety, latency, scalability and cost; familiarity with technologies such as OpenAI, Anthropic, Bedrock, Azure OpenAI, LangGraph, LangChain, Semantic Kernel or vector databases. Strong collaboration and communication skills. A bachelor's degree in a relevant technical field or equivalent practical experience.
Preferred
A relevant master's degree or PhD; experience in educational technology or enterprise-scale AI platforms; familiarity with psychometrics, proficiency modeling, Bayesian methods, item response theory or educational measurement; and contributions to research, patents, open source or industry thought leadership.
The role description states “Remote, United States,” although the listing header identifies Springfield, IL and labels the workplace Hybrid. The full-time salary range is $150,000–$190,000, with eligibility for an annual incentive program.