AI Scientist

Position Overview 

The AI Scientist (Specialist) builds and refines machine learning models within Pearson's AI Center for Enablement (C4E). The C4E is a small team that helps the wider business make good decisions about AI and get things built well. This role sits at the practical end of that work: taking real problems from product, data, and engineering teams and turning them into working prototypes and models. 

The Specialist works with a good degree of autonomy on defined problems, collaborates closely with the teams they are building for, and shares what they learn along the way. This is a hands-on role for someone who is comfortable moving between exploration and delivery, and who brings their own technical judgement rather than waiting to be told what to build. 

Key Responsibilities 

• Implement, fine-tune, and evaluate machine learning models (classic ML and generative AI) based on what each problem requires. Including development of agents. 
• Work closely with product managers, data engineers, and domain experts to understand what teams need, translate that into a clear technical approach, and deliver concrete solutions. 
• 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. 

Expected Deliverables 

• Working prototypes and model implementations that meet what the team actually needed, not just the literal brief. 
• Timely delivery with clear communication when something is at risk of slipping. 
• Experiment reports covering methodology, results, failure modes, and next steps. 
• Documentation clear enough for a teammate to pick up and continue work. 
• Contributions to code reviews and team learning that leave shared knowledge better than it was found. 

Required Qualifications 

• Hands-on experience training, evaluating, and iterating on ML or deep learning models. 
• Development and  
• Strong Python skills and familiarity with the standard ML stack (e.g. PyTorch, TensorFlow, 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. 

Preferred Qualifications / Nice to Have 

• Experience with LLMs or generative AI, including fine-tuning, evaluation, or prompt engineering. 
• Familiarity with educational technology, assessment, or language learning domains. 
• Experience working with unstructured data: text, audio, or multimodal inputs. 
• Some grounding in responsible AI principles and how they apply in practice. 
• Exposure to cloud ML tooling (AWS, Azure, GCP) or MLOps workflows. 

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