Cambridge Residency Programme: AI Researcher in Interactive Generative AI systems

## About the role As a part of the team, you will be working with world-class researchers, engineers and design experts, to create novel generative AI models and capabilities that support creative processes. * Collaborate to implement and evaluate new approaches, both conceptual and practical, within existing or new emerging cross-company collaborations. * Contribute to the team's goals by generating novel ideas, implementing prototypes, running experiments, utilizing multi-node GPU infrastructure, and rigorously evaluating all ideas with an open mindset. * Design efficient experimentation workflows to accelerate research iteration across the team. * Share knowledge and learn from others, participating in design decisions, presenting your ideas, doing pair programming, reviewing code, etc. * Help to continuously improve our ways of working and the quality of our work, giving constructive feedback, understanding other points of view, suggesting new processes, etc. * Distill insights into effective communications, such as research papers and presentations, or other forms of communications (for example blog posts) to reach technical and general audiences. ## Requirements * Master's or PhD degree in Artificial Intelligence, Computer Vision, Machine Learning, Natural Language Processing, or a related field, OR equivalent experience. * Research experience in deep learning, including understanding state-of-the-art model architectures, data pipelines, and methods of evaluation. * Research experience in at least one of the following or related areas: * Generative models (video and world models, diffusion, autoregressive models, controllability) * Multi-modal modelling * Training and fine-tuning large multimodal models or AI systems for multi-step interaction, such as agentic workflows, orchestration, tool-using systems, memory, or adaptive workflows * Hands-on knowledge of deep-learning frameworks, with strong software engineering practices and a commitment to writing clean, maintainable and well-tested code. * Experience publishing academic papers and/or demonstrated impact through ML research tooling or open-source contributions. * Experience scaling deep learning workloads to maximise responsible utilization of multi-node GPU clusters. * Experience designing or implementing AI workflows, such as automated training, evaluation, or experimentation pipelines. * Familiarity with game engines or creative AI tooling and/or experience applying AI in interactive settings.