Staff Applied Scientist – Personalisation

Staff Applied Scientist – Personalisation

Requirements

• A Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent practical experience)
• Direct working experience on interpretability, personalisation techniques, recommender systems, or large-scale data analytics
• Strong expertise in machine learning algorithms, statistical methods, and optimisation techniques
• Have a strong track record of scientific research (in any field) background
• You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results
• Experience (or desire to be) working in multi-disciplinary teams

Responsibilities

• Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights
• Develop and evaluate algorithms that enhance the transparency of black-box models, enabling better understanding of decision-making processes
• Performing fine-tuning and reinforcement learning to teach language models how to interact with new information architectures
• Building ‘hard’ eval sets to help identify failure modes of how language models work with legal data
• Build infrastructure for running experiments and visualising results
• Work with colleagues to communicate results internally and publicly
• Stay updated with the latest research in machine learning, AI, and interpretability or personalisation to bring innovative solutions to the table
• Mentor junior researchers and contribute to building a collaborative, knowledge-sharing culture

Preferred Qualifications

No preferred qualifications provided.