Amazon Senior Applied Scientist Rufus Features Science 

United Kingdom, England, London 

930279042

This role focuses on developing conversation-based, multimodal shopping experiences, utilizing multimodal large language models (MLLMs), generative AI, advanced machine learning (ML) and computer vision technologies.Key job responsibilities
As an Senior Applied Scientist on our team, you will be responsible for the research, design, and development of new AI technologies that will shape the future of shopping experiences. You will play a critical role in driving new ideas, roadmaps, aligning with stakeholders and partner teams, leading the development of multimodal conversational systems, building on large language models, information retrieval, recommender systems, knowledge graphs and computer vision. You will handle Amazon-scale use cases with significant impact on our customers' experiences. You will collaborate with scientists, engineers, and product partners locally and abroad.
You will:
- Take product ideas for new features and turn them into tech solution designs and roadmaps, evaluating the feasibility and scalability of possible solutions.
- Lead the development of scalable language model centric solutions for shopping assistant systems based on a rich set of structured and unstructured contextual signals using deep learning, ML, computer vision and MLLM techniques, and considering memory, compute, latency and quality.
- Drive end-to-end MLLM projects that have a high degree of ambiguity, scale and complexity, developing the most critical or challenging parts of the systems yourself (hands on).
- Perform offline and A/B test experiments, optimize and deploy your models into production, working closely with software engineers.
- Establish automated processes for large-scale model development, model validation and serving.
- Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports and publish your work at internal and external conferences.

- PhD, or Master's degree
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.