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