Amazon Senior Applied Scientist Rufus Features Science 

United Kingdom, England, London 

705477306

Key job responsibilities
* Lead the development of state-of-the-art agentic LLM solutions for conversational shopping, considering scalability, latency, and quality.
* Design and implement innovative AI technologies that push the boundaries of Natural Language Processing (NLP), Generative AI, MLLMs/VLMs, Machine Learning (ML), Retrieval-Augmented Generation (RAG), and Reinforcement Learning (RL).
* Lead science roadmaps spanning multiple areas, working with senior leaders and stakeholders.
* Develop and evaluate production Agentic AI systems for real customer use cases, focusing on LLM-based conversational interfaces and multimodal interactions.
* Drive end-to-end MLLM projects with high ambiguity, scale, and complexity, taking a hands-on approach to the most critical aspects.* Communicate progress and results internally to both technical and non-technical audiences and publish at top-tier conferences.

- PhD
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with neural deep learning methods and machine learning
- Experience in building machine learning models for business application
- PhD in NLP, Information Retrieval, Machine Learning, or related fields (or equivalent experience), with 6+ years of industry experience.
- Extensive experience with deep learning-based NLP, IR, and MLLM/VLM methods.
- Strong track record in addressing real-world problems using ML and NLP.
- Expertise in developing and owning production ML models and systems, particularly those involving LLMs.
- Proficiency in Python and experience with production-level implementation.
- Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
- Familiarity with cloud computing platforms, particularly AWS.
- Demonstrated ability to lead and shape scientific roadmaps across multiple areas, collaborating with product, science, and engineering managers.
- Knowledge of recent advancements in AI agents, including multi-agent systems and agent evaluation frameworks.

- Experience with popular deep learning frameworks such as MxNet and Tensor Flow.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Good publication record at top-tier venues such as ACL, NAACL, EMNLP, SIGIR, ICLR, NeurIPS, or similar.
- Understanding of e-commerce and recommendation systems.
- Excellent communication skills, solid work ethic, and a strong desire to write production-quality code.