Applied Scientist II, Strategic Account Services (SAS)
Amazon · London, United Kingdom · Project/Program/Product Management--Technical
About this role
Amazon is hiring a mid-level Applied Scientist in the machine learning function based in London, United Kingdom. The posting calls out experience with Java, C#, LLMs, Deep Learning.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- London, United Kingdom
- Department
- Project/Program/Product Management--Technical
- Posted
- Oct 27, 2025
More roles at Amazon
Job description
from Amazon careersAmazon Strategic Account Services (SAS) Tech Organization is looking for an Applied Scientist Applied Scientist who can autonomously drive scientific innovations from research to production, developing sophisticated AI solutions that serve both Amazon's global seller base and internal Marketplace Consultants. Working in a highly collaborative environment, you'll leverage expertise in machine learning, operations research, and statistics to translate theoretical advances in LLMs, probabilistic modeling, and optimization into practical applications. The role demands strong capabilities in prototyping and iterative improvement, bridging cutting models with real-world applications while maintaining scientific rigor and measurable business impact. Key job responsibilities - Lead the development of sophisticated AI solutions leveraging deep learning, LLMs, and advanced machine learning techniques to transform both seller operations and internal consultancy capabilities at scale - Define and drive long-term scientific vision for the organization, translating complex business challenges into innovative technical solutions that advance the state-of-the-art in applied machine learning - Design and implement advanced ML architectures combining multiple learning paradigms - from reinforcement learning and causal inference to predictive modeling - to tackle critical marketplace challenges - Architect next-generation recommendation and optimization systems that handle complex multi-dimensional constraints while maintaining robustness and interpretability at scale - Drive end-to-end development…