Job Description
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? Our team is Amazon's ambitious initiative to pioneer AI development practices that will fundamentally transform how we build software at scale.
? 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
* Working with the latest technologies and large language models
* Culture of experimentation and innovative thinking
? What We’re Looking For
You’ll 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’re comfortable working in ambiguous spaces, exploring emergent behaviors, and collaborating with designers, prototypers, and engineers to test hypotheses in live, evolving systems.
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.
We’re looking for applied scientists with experience in areas like:
+ Information retrieval
+ Context and memory modeling
+ Agent behavior and autonomy
+ Code understanding and generation
+ Human-AI interaction
? What You'll Do
* Prototype new ways of working with LLMs in engineering workflows
* Help shape and evaluate scientific approaches behind agentic systems and engineering tools
* Move fast with engineers and prototypers to guide design decisions and model choices
* Make sense of unexpected model behavior and guide what to try next
? Impact
Your work will surface where LLMs can meaningfully change how Amazon engineers build.
You’ll help the team uncover which surprises are worth pursuing and where to push beyond what’s already known.
✨ Always find a way ???⭐️
BASIC QUALIFICATIONS
- 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 patents or publications at top-tier peer-reviewed conferences or journals
- Strong analytical, mathematical, and coding skills (e.g., Python, C++, or Java)
- Previous work on agents or LLMs
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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