Principal Applied Scientist, Amazon Q

Job ID: Amazon Development Center U.S., Inc.

Amazon Web Services (AWS) is looking for a Principal Applied Scientist to join the Amazon Q team. Q 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.

You'll work on 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.

Key Job Responsibilities

• Lead the design and development of foundational models and intelligent agent architectures tailored to enterprise use cases.
• Partner with engineering, product, and UX teams to integrate generative AI into the Amazon Q assistant.
• Drive experimentation to improve model accuracy, safety, latency, and cost efficiency.
• Mentor other scientists and engineers, helping raise the technical bar across the team.
• Contribute to and publish in top tier conferences or file patents based on novel research contributions.

About the Team

Amazon Q is a generative AI powered assistant that helps employees become more productive by answering questions, generating content, summarizing data, and automating workflows using enterprise information. Our team builds the intelligence behind Q, leveraging large language models, retrieval augmented generation, and agentic architectures to orchestrate complex workflows securely and at scale. We're focused on building systems that reason, plan, and act across multiple modalities and business tools. As part of AWS's broader Agentic AI initiative, we're shaping the future of enterprise AI-empowering organizations to solve problems faster, reduce operational overhead, and unlock new levels of efficiency and decision making.

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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Your compensation will include a base salary ranging from $179,000 to $309,400 per year depending on geographic market, plus potential equity, sign on payments, and other benefits. For more information, visit the Amazon employee benefits page. This position will remain posted until filled. Applicants should apply via our internal or external career site.