Mid Level Systems Development Engineer Jobs

The Product Identity and Semantic Matching team in WW Amazon Stores is looking for a passionate and talented Senior Software Development Engineer with deep expertise in building large-scale distributed systems and AI/ML-powered applications to help build the world's best product catalog. In this role, you will own end-to-end architecture and delivery of systems that establish product identity and enable high-fidelity product matching and product relationship at Amazon scale. You will work in a collaborative environment where you can design and build systems that process 1B+ daily transactions, leverage AI/ML solutions, and directly impact every Amazon customer's shopping experience.

An information-rich and accurate product catalog is a strategic asset for Amazon. Platform Matching builds the canonical product identity and matching system that powers Amazon's universal catalog—eliminating duplicates, preventing abuse, and enabling seamless integration of the world's product data to power shopping experiences and trusted discovery for customers worldwide. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages), multitude of input sources (millions of sellers contributing product data with different quality), and varied relationship types.

This role combines deep technical leadership with the ability to influence across organizational boundaries, making strategic architectural decisions while maintaining the highest standards for reliability and scalability. Successful candidates are passionate about translating high-level, ambiguous business goals into software solutions that enable multiple businesses and are comfortable taking initiative alongside top-notch software developers and scientists.

Key job responsibilities
As a Senior SDE on the team, you will own the design and delivery of large-scale systems that power product identity and matching. Your responsibilities include:

• Owning end-to-end design, implementation, and operational excellence for complex features spanning multiple services and teams
• Leading architecture decisions for large-scale systems handling hundreds of millions of transactions daily with AI/ML components
• Navigating technical and organizational ambiguity; translating vague requirements into implementable solutions under tight schedules
• Designing systems from scratch in fast-evolving domains; integrating with Amazon's established ecosystem including LLM deployments, agentic systems, and workflow optimization pipelines
• Building and optimizing inference infrastructure for multimodal models at scale, including embedding-based systems, and cascaded inference architectures
• Driving change across teams through technical influence, unblocking others and establishing architectural consistency across engineering and science workstreams
• Mentoring junior and mid-level engineers; providing meaningful feedback through code reviews and technical guidance
• Communicating effectively across multiple stakeholders; making strategic trade-offs balancing velocity with long-term system health
• Partnering closely with Applied Scientists to translate ML research into production systems that process billions of records

About the team
The Product Identity and Semantic Matching team has a mission to advance state-of-the-art GenAI to deeply understand and uniquely identify every product at Amazon scale. We build the canonical product identity and matching system that powers Amazon's universal catalog—eliminating duplicates, preventing abuse, and enabling seamless integration of the world's product data to power shopping experiences and trusted discovery for customers worldwide. We push the boundaries of advanced ML, multimodal LLMs, and generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our e-commerce business. We solve foundational challenges including schema-agnostic multi-modal understanding, efficient inference at order-of-magnitude cost reduction, and rapid innovation through automated experimentation powered by agentic systems.