Lead Specialist, AI Scientist

Stack

Responsibilities

• ▹Design and build speech language models for spoken response understanding, pronunciation analysis, fluency, and communicative effectiveness
• ▹Develop and evaluate automated scoring and feedback pipelines for speaking tasks, for learner-facing and hiring-facing applications
• ▹Train, fine-tune, and evaluate acoustic models, cascading models, speech-to-speech models, speech LMs, and scoring models
• ▹Design experiments and conduct quantitative performance, reliability, and validity analyses
• ▹Perform error analysis and inter-rater reliability studies on ASR outputs, spoken features, and scoring behaviors
• ▹Collaborate with product, UX, and assessment scientists to integrate models into interactive experiences
• ▹Apply responsible AI principles to speech systems: fairness across accents and dialects, transparency of feedback and scores
• ▹Support model monitoring and governance in production environments
• ▹Act as technical lead for an AI conversational assessment product, partnering closely with a Product Manager

Requirements

• ▹Master's or PhD in Computer Science, Electrical Engineering, Speech & Language Processing, Applied Linguistics, Language Assessment, or equivalent applied experience
• ▹Hands-on experience building speech recognition, spoken language understanding, or automated scoring systems
• ▹Strong programming skills in Python, with experience using PyTorch or similar ML frameworks for speech and language modeling
• ▹Solid grounding in machine learning, statistics, and experimental design, especially for model evaluation
• ▹Experience with modern neural speech models and large language models, including fine-tuning and evaluation
• ▹Expertise in model evaluation metrics relevant to speech and assessment: accuracy, reliability, validity, fairness
• ▹Familiarity with responsible AI practices: bias analysis, interpretability, governance for user-impacting systems

Soft skills

About the company

Pearson develops AI systems for language assessment and skills evaluation, for both learner-facing and hiring-facing use cases.