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Amazon Software Development Engineer Automated Brand Protections 
United States, Washington, Seattle 
408073757

10.06.2024
DESCRIPTION

As part of our team, you will get to work on billion-scale search space to detect and action on bad listings. You will get to take GenAI(LLM) models to production and use it to clean up the catalog. You will be building low latency, high throughput systems to safeguard the Amazon catalog from fraud. You will work closely with scientists to productionize models. You will also work with a variety of stakeholders including business and engineering teams across the organization. You should be a self-starter with keen attention to detail and be excited to work in a fast-paced environment. Above all, you should be passionate about working on problems at scale and solving them creatively.Core responsibilities
Design, develop and maintain cutting edge, highly reliable and scalable distributed systems.
Work with other engineers and data scientists to design, prototype and implement new features.Mentor other engineers.
Seattle, WA, USA

BASIC QUALIFICATIONS

- 2+ years of non-internship professional software development experience
- 1+ 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
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence


PREFERRED QUALIFICATIONS

- 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
- Experience building complex, scalable, high-performance software systems that have been successfully delivered to customers
- Experience with GenAI and using LLMs