Applied Scientist, StoreGen

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience in software development, or experience demonstrating software engineering skills in a previous intership, work experience, coding competitions, or publications and experience in Kafka
- Experience that includes strong analytical skills, attention to detail, and effective communication abilities, or experience in software development
- Previous work on agents or LLMs
- On-device model development

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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
Join our Generative AI focus team to innovate Software Development at Amazon

Are you ready to be part of a team that's redefining the future of software engineering? We are seeking an Applied Scientist to lead research in agentic AI systems for developer tooling at Amazon. You will join a small, focused cross functional team investigating how autonomous agents can reliably perform complex tasks on behalf of software engineers.

🎯 Our Mission
We’re building the next generation of development tools, uncovering how LLMs can originate new ways of thinking, building, and collaborating.


💫 What Makes Us Different
* Ground-floor opportunity to shape the future of software development
* Direct impact on Amazon's global engineering practices
* Culture of experimentation and innovative thinking

🔍 Research Areas
* Low-latency tool-calling architectures: Compact models optimized for response time
* Efficient tool selection and delegation across model hierarchies.
* Test-time observational learning: Methods for agents to acquire task knowledge through observation of user behavior.
* Comparative analysis against supervised fine-tuning approaches when labeled data is available.
* Action safety classification: Real-time classification of agent-initiated actions to assess risk and determine when human approval is required. * Calibration of confidence thresholds for autonomous execution.
* Structured representations for agent interaction: Compilation of web application structure (static assets, API schemas, interaction patterns) into formats optimized for agent parsing and execution across client-side and server-rendered environments.

💡 What You'll Do
* Prototype new ways of working with LLMs in engineering workflows
* Lead the research, design, and development of AI technologies that challenge conventional software practices and uncover new ways for engineers to build and collaborate.
* You explore where LLMs behave in unexpected yet promising ways, especially in tasks like context retention, code reasoning, or agent behavior , and use those moments to uncover new AI-native workflows.


🌱 Who Thrives Here
You operate well in cross-functional settings where research, design, and engineering inform each other continuously. You are comfortable iterating quickly, testing ideas in real systems, and adjusting course based on what you observe.

You may have worked in applied research environments where scientists are embedded with designers and engineers throughout the product development process rather than siloed from it.


✨ Always find a way 🔮💎🍔⭐️