Specialist, AI Scientist
US$150,000 – US$190,000 / yr base salary
Posted 9 Oct 2026
Advertised as “Lead Specialist, AI Scientist”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- People management
- Employment
- Hybrid
- Contract
- Full-time
| Base salary | US$150,000 – US$190,000 / yr |
|---|
Field of studyArtificial Intelligence, Computer Science, Data Science, Engineering, Machine learning
Study levelBachelor's
Field of studyArtificial Intelligence, Computer Science, Machine learning, Statistics (or related)
Study levelMaster's or higher
Lead Specialist, AI Scientist
Location: Remote, United States
About the Role
We are seeking a strategic and hands-on Lead Specialist- AI Scientist to design, build, deploy, and scale production AI/ML capabilities that power Pearson's learner intelligence, knowledge graphs, recommendations, personalized learning experiences, and next-generation AI products.
This role bridges AI research, data science, software engineering, and product delivery. The successful candidate will lead the development of machine learning, Generative AI, LLM, agentic AI, and knowledge graph solutions, taking them from concept and experimentation through production deployment and continuous improvement. The ideal candidate combines deep technical expertise with a strong execution mindset and a passion for delivering measurable learner and business outcomes.
What You'll Do
- Lead the design, development, deployment, and operation of production AI capabilities supporting learner intelligence, personalization, recommendations, knowledge graphs, and AI-powered learning experiences.
- Design and deliver Generative AI, LLM, retrieval-augmented generation (RAG), and agentic AI solutions that create measurable product and business impact.
- Build reusable AI platform capabilities, services, APIs, and workflows that accelerate AI adoption across Pearson products.
- Own the end-to-end AI delivery lifecycle, from experimentation and prototyping through deployment, monitoring, evaluation, and continuous improvement.
- Establish scalable MLOps and AIOps practices for model training, deployment, observability, governance, reliability, and operational excellence.
- Partner closely with Product, Engineering, Design, Learning Science, and Data Science teams to identify opportunities and deliver impactful AI-powered capabilities.
- Evaluate emerging AI technologies, foundation models, and architectural approaches while balancing quality, safety, scalability, latency, and cost.
- Establish best practices for responsible AI, model evaluation, prompt engineering, agent evaluation, and AI governance.
- Mentor engineers and data scientists and help elevate AI engineering capabilities across the organization.
- Communicate technical strategy, architecture decisions, trade-offs, risks, and outcomes to stakeholders across the business.
Expected Results:
- Production-ready learner intelligence, recommendation, and knowledge graph capabilities powering personalized learning experiences.