Amazon's Selection Monitoring team is responsible for making the biggest catalog on the planet even bigger. Our systems process billions of products from millions of sources to algorithmically find products not already sold on Amazon and programmatically add them to the Amazon catalog. We apply parallel processing, AI/GenAI, machine learning and deep learning algorithms to evaluate products and brands in order to identify and prioritize products and brands to be added to Amazon's catalog. The datasets produced by our team are used by teams across Amazon to improve: product information, search and discoverability, pricing, and delivery experience. Our work involves building state-of-the‑art Information Retrieval (IR) systems to mine the web and automatically create structured entities from un‑structured/semi‑structured data. We constantly stretch the boundaries of large‑scale distributed systems, elastic computing, big data, artificial intelligence and SOA technologies to tackle challenges at Amazon's global scale.
Come join us in our journey to make everything – and yes, we do mean everything – that anyone wants to buy, available on Amazon!
We are looking for an SDM with strong technical knowledge, an established background in engineering large‑scale software systems, and passion for solving challenging problems. The role demands a high‑performing and flexible candidate who can take responsibility for the success of the team, systems, and drive solutions from definition to launch to achieve results in a fast‑paced environment.
The SDM on this team will play a key role in defining charter/scope, executing, and delivering innovative solutions to drive complete and correct selection on Amazon's eCommerce platform worldwide. The candidate needs a high sense of ownership, the ability to take on ambiguous and ambitious goals, and must lead a highly talented team to achieve results. This role exposes you to solving problems at scale in areas of orchestration, automated insights, SLA management, large‑scale data processing, and distributed systems of cloud scale.
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