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

• Implement, fine-tune, and evaluate machine learning models (classic ML and generative AI) based on what each problem actually requires. • Work closely with product managers, data engineers, and domain experts to understand what teams need, translate that into a clear technical approach, and deliver something concrete. • Contribute to model design discussions with a clear point of view on tradeoffs: accuracy, latency, cost, and data availability. • Participate in code reviews as both reviewer and contributor, keeping quality and shared standards consistent across the team. • Document experiments thoroughly: what was tried, what the results showed, what the limitations were, and what should happen next. • Share technical knowledge with the team, especially when working with new methods or tools, through write-ups, short sessions, or whatever fits the situation. • Engage the Responsible AI, Data, and Platform teams early so that solutions meet the right standards before problems accumulate.

🎯 Requirements

• Hands-on experience training, evaluating, and iterating on ML or deep learning models. • Strong Python skills and familiarity with the standard ML stack (e.g. PyTorch, scikit-learn, Hugging Face). • Ability to take an ambiguous brief, ask the right clarifying questions, and turn it into a sensible technical plan. • Able to explain technical decisions clearly to people who are not data scientists, and willing to adapt based on feedback. • Good instincts for what actually matters in a given problem, with experience using experiment tracking tools (e.g. MLflow, DVC, or equivalent).