- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
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 https://amazon.jobs/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.
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, Bellevue - 143,700.00 - 194,400.00 USD annually
We are seeking a Software Development Engineer (SDE) to join our Amazon Manager eXperience (AMX) Science Generative AI Engineering Team. This interdisciplinary team combines the talents of science and engineering to develop and deliver solutions that measurably improve the lives, wellbeing, and value of work for Amazonians.
You'll develop innovative products built on GenAI capabilities, analytics platforms, and web applications integrated with other Amazon systems. This position offers unique opportunities to learn about Generative AI with hands-on experience while collaborating with applied and research scientists, academic scholars, and machine learning experts to craft and launch services embodying diverse scientific models.
The ideal candidate will have:
- Robust software engineering fundamentals and adeptness in system architecture
- Familiarity with Machine Learning lifecycle management and enhancing LLM responses through methodologies like Retrieval Augmented Generation (RAG) and Prompt Engineering
- Exposure to ML system design, frameworks, GenAI application design/development, and industry best practices
- Passion for innovation, leading-edge technology, and contributing to a high-impact field
Key job responsibilities
1. Design and develop scalable, high-performing software solutions that power Machine Learning and Large Language Model applications, ensuring operational excellence throughout the development lifecycle
2. Build and maintain distributed systems that handle complex data processing needs, from data lakes to GenAI tools, ensuring security, reliability, and performance
3. Collaborate across disciplines with economists, scientists, and engineers to translate scientific models into practical applications that improve work experiences for Amazonians
4. Provide meaningful code reviews and contribute to the full software development lifecycle, including scoping, design, testing, deployment, and maintenance
5. Document software architecture, operation procedures, and security considerations while actively training new team members on these systems
A day in the life
Your day typically involves designing and coding solutions for complex distributed systems that power our ML and GenAI applications. You'll attend standups to align with your cross-functional team, review code from peers, and collaborate with scientists and ML experts to implement scientific models. Time will be split between developing new features, improving existing systems, and addressing operational concerns to ensure our applications run smoothly and securely
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