Principal Applied Scientist, AWS Agentic AI
a w sSeattle (WA)
Principal Applied Scientist, AWS Agentic AI
Posted today
a w sSeattle (WA)
Computer and Information Research ScientistsOther Scientific and Technical Consulting ServicesJobs in Seattle, WA
SENIORITY
Lead
About the role
Principal Applied Scientist Amazon Web Services (AWS) is looking for a Principal Applied Scientist to join the Quick Science team. Quick is AWS's enterprise generative AI assistant that helps users answer questions, summarize documents, generate content, take actions, and automate workflows using information across enterprise systems. As a key member of this team, you will lead research and development efforts in generative AI and Agentic AI to enable intelligent agents that perform complex reasoning, automate multi-step workflows, and make enterprise users significantly more productive. Key job responsibilities include building and optimizing multi-modal foundation models, training and fine-tuning state-of-the-art LLMs, and architecting systems that scale efficiently across domains. This role blends science leadership, hands-on innovation, and deep collaboration with engineering teams to bring research into production.
Basic Qualifications:
PhD in Machine Learning, Computer Science, Electrical Engineering, or a related technical field OR a Master's degree with 5+ years of relevant industry or research experience.- Industry experience developing machine learning models for real-world applications.- Experience with generative AI, including model training or building systems with pre-trained foundation models.- Proven record of peer-reviewed publications or granted patents in AI/ML.- Proficiency in Python or similar programming languages.- Experience in at least one of the following areas: natural language processing (NLP), large language models (LLMs), computer vision, or Agentic AI.Preferred
Qualifications:
Experience applying generative AI to enterprise or multi-modal tasks (e.g., code generation, document understanding, or task planning).- Strong understanding of agentic architectures, autonomous systems, or task orchestration.- Hands-on experience with scalable ML infrastructure, distributed training, or optimization of large models.- Deep knowledge of AI safety, hallucination mitigation, or retrieval-augmented generation (RAG).- Experience mentoring junior scientists and influencing cross-functional stakeholders.- Ability to think strategically and communicate complex technical topics to non-experts, including senior leadership.- Track record of shipping scientific innovations into customer-facing products at scale.
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