Lead Specialist, AI Scientist

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Pearson VUE

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1994

📚 Education

🛍️ eCommerce

☁️ SaaS

Education • eCommerce • SaaS

Pearson VUE is a global leader in computer-based testing, providing a wide range of credentialing and certification exams for various industries. They support test-takers and test owners by offering resources, scheduling options, and accommodations to ensure equitable access to testing. Their mission is to empower candidates and enrich communities through the delivery of high-stakes exams that validate professional skills and knowledge, contributing to career advancement and industry standards.

📋 Description

• Lead AI modelling projects end-to-end, from problem definition through to a validated solution that a partner team can understand, adopt, and maintain. • Advise product, data, and engineering teams on AI solution design, including model selection, data requirements, architecture decisions, and the tradeoffs involved in each. • Serve as a technical reference across the C4E and its partner teams: review approaches, answer hard questions, and provide a grounded second opinion on high-stakes decisions. • Adapt model designs and methods based on partner feedback and validation results, balancing technical rigour with practical constraints. • Identify where the team's modelling practices could be stronger and act on it: better evaluation approaches, shared templates, clearer processes. • Create reusable technical resources such as design patterns, evaluation frameworks, and model cards, and actively facilitate knowledge sharing across disciplines. • Collaborate with the Responsible AI, Data, Platform, and Security teams, ensuring the right people are involved at the right stage and feeding recurring patterns or gaps back to them. • Support partner adoption by producing documentation and handover materials that are genuinely usable, and staying involved until teams are confident with what has been built.

🎯 Requirements

• Substantial hands-on experience building and shipping ML or deep learning models, including complex projects with real production requirements. • Strong Python skills and fluency across the ML stack (e.g. PyTorch, Hugging Face), with a solid command of experiment design and rigorous evaluation methodology. • Demonstrated ability to advise on AI solution design and communicate tradeoffs clearly to both technical and non-technical audiences, including senior stakeholders. • Track record of adapting technical approaches based on feedback and new evidence. • Experience working across disciplines (data, product, research, compliance) on AI projects of meaningful scope and complexity. • Clear, concise technical writing and strong facilitation skills. • Experience with generative AI, including LLM fine-tuning, RAG architectures, prompt engineering, or evaluation of LLM-based systems. (Nice to Have) • Familiarity with educational technology, assessment, speech processing, or language learning domains. (Nice to Have) • Substantive exposure to responsible AI in practice: working through fairness, bias, or explainability problems on real projects, not just in theory. (Nice to Have) • Experience improving how a data science or ML team works, not just individual output. (Nice to Have) • Familiarity with MLOps tooling and multi-team AI governance workflows. (Nice to Have) • Prior experience in an advisory or enablement role. (Nice to Have)

🏖️ Benefits

• N/A