Applied Research Scientist

Job description

Role Overview We are seeking a Senior Research Scientist with deep expertise in Large Language Models (LLMs) and modern machine learning to help build and scale the Glance LLM, a domain-specific model focused on fashion intelligence, recommendation, and conversational commerce. This role sits at the intersection of research, data, and systems , with responsibility for developing and evolving LLM capabilities from foundation model adaptation through to domain-specific pretraining and optimisation. You will contribute to the design of training pipelines, data strategies, and model architectures , with a focus on translating cutting-edge research into production-ready systems. This is a hands-on role for someone with a strong research background who is motivated to see their work deployed at scale, shaping real-world user experiences. Key Responsibilities • Design and develop domain-specific LLM capabilities , including fine-tuning, continued pretraining, and alignment strategies • Build and optimise end-to-end LLM pipelines , including data ingestion, tokenisation, training, evaluation, and deployment • Lead research in representation learning for structured domains , particularly fashion, commerce, and multimodal data • Develop approaches for retrieval-augmented generation (RAG) , tool use, and grounded reasoning over product catalogues • Work with large-scale datasets, including data curation, filtering, and synthetic data generation • Contribute to model evaluation frameworks , including both automated benchmarks and human-aligned evaluation • Collaborate with ML Engineers to productionise models , ensuring scalability, cost efficiency, and reliability • Evaluate and integrate advances from leading models such as Gemma and Qwen • Mentor junior researchers and contribute to a strong applied research culture Required Qualifications • PhD in Machine Learning, Natural Language Processing, Artificial Intelligence, or a related field • Strong publication record in top-tier venues such as NeurIPS, ICML, ACL, EMNLP, or ICLR • Deep understanding of transformer architectures, scaling laws, and LLM training methodologies • Experience with fine-tuning, instruction tuning, or continued pretraining of large models • Strong programming skills in Python, with experience in frameworks such as PyTorch • Experience working with large-scale datasets and distributed training systems • Ability to operate across research and engineering, with a focus on delivering practical impact Preferred Qualifications • Experience building or adapting domain-specific LLMs (e.g., for recommendation, commerce, or structured reasoning) • Familiarity with retrieval-augmented systems (RAG) and vector databases • Experience with model distillation, quantisation, or optimisation for deployment • Exposure to multimodal models (text + image) • Experience with evaluation design , including human alignment studies • Contributions to open-source LLM frameworks or research artefacts What You’ll Work On • Building the Glance LLM , a domain-specific model optimised for fashion understanding and personalisation • Developing systems that combine language understanding with product intelligence , enabling richer user interactions • Designing scalable pipelines that balance model quality, cost, and latency • Contributing to a long-term roadmap toward greater ownership of LLM capabilities , including pretraining and model optimisation • Supporting integration with broader systems, including image generation and on-device AI Why This Role Matters At Glance, the LLM is not just a conversational interface, it is the intelligence layer that connects users, products, and experiences. The ability to deeply understand fashion, context, and user intent is central to delivering personalised, high-quality experiences at scale. This role is critical in defining how that intelligence is built, adapted, and evolved over time, balancing rapid progress with long-term capability ownership and differentiation. What We Offer • Opportunity to work on cutting-edge LLM research with real-world deployment at scale • A strong applied research environment balancing innovation and production impact • Access to large-scale datasets and compute infrastructure • The ability to shape a domain-specific AI platform from first principles • Competitive compensation and growth opportunities