AI Research Senior Associate

Join us to push the boundaries of language and multimodal understanding. You will collaborate with talented colleagues to design and evaluate advanced models that shape the future of AI. We value your expertise and encourage creative solutions to complex challenges. Your work will have a direct impact on our technology and the broader industry. Experience a supportive environment where your growth and contributions matter. As an NLP Research Scientist in our London-based AI Research team, you will lead innovation in large language model research and development. You will design, implement, and evaluate state-of-the-art models, including multimodal and agent-based systems. Your role will focus on advancing the frontiers of language and multimodal understanding. You will work closely with experts in NLP, deep learning, and AI infrastructure. Together, we will drive impactful research and real-world applications.

Lead research in large language model pre-training and post-training Design and implement novel algorithms for language and multimodal models Evaluate and optimize model performance using advanced techniques Collaborate with team members to develop AI agents and multimodal systems Contribute to the development of robust ML infrastructure for model training and deployment Publish research in top conferences and journals Explore parameter-efficient fine-tuning and retrieval-augmented generation Advance document AI and visually-rich document understanding Develop and assess dialogue systems, question answering, and summarization models Generate synthetic data and extract information for knowledge graphs Apply state-of-the-art generative AI techniques to real-world challenges Required Qualifications, Capabilities, and Skills: Expertise in NLP frameworks such as HuggingFace Transformers Proficiency with deep learning libraries including PyTorch Strong knowledge of language modeling, sequence-to-sequence architectures, and transformer-based models Skilled in ML infrastructure for large-scale data processing, model training, evaluation, optimization, and deployment Demonstrated ability to design, implement, and assess novel algorithms beyond standard toolkits Experience with generative AI techniques and related concepts Preferred Qualifications, Capabilities, and Skills: Experience with parameter-efficient fine-tuning (PEFT) Familiarity with retrieval-augmented generation (RAG) Knowledge of multimodal LLMs and visual language models (VLM) Background in document AI and visually-rich document understanding (VRDU) Experience developing AI agents and dialogue systems Publication in conferences such as ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, AAAI, IJCAI, CVPR, ICCV, ECCV, ICDAR, KDD, SIGIR Research in areas including question answering, summarization, search, synthetic data generation, information extraction, and knowledge graphs