Software Development Engineer, Vector Search

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- 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

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USA, WA, Bellevue - 143,700.00 - 194,400.00 USD annually
The OpenSearch Vector Search team builds and operates the core vector search engine that powers recommendations, semantic search, and multi-modal search capabilities across Amazon's flagship services including Amazon Prime, Amazon Retail, Amazon Music, and Alexa. Our technology enables Amazon OpenSearch Service to handle large-scale vector search operations at billions of high-dimensional vectors, contributing to the advancement of vector databases and AI/ML infrastructure. We are at the forefront of building the retrieval and reasoning substrate for modern enterprise AI systems.

We are looking for a passionate and talented Software Development Engineer (SDE II / L5) to join our Vector Search team. You will design, build, and optimize high-performance vector search systems that operate at massive scale. This role offers the opportunity to work on cutting-edge problems in approximate nearest neighbor (ANN) search, GPU-accelerated indexing, new algorithms to improve the recall and performance — all within one of the most widely adopted open-source search platforms in the world.

Key job responsibilities
- Design and implement high-performance vector search algorithms (HNSW, cluster-based ANN, hybrid approaches) for billion-scale datasets
- Develop and optimize GPU-accelerated vector indexing and search capabilities
- Build and enhance the k-NN plugin for OpenSearch, including support for new distance metrics, quantization techniques, and memory-efficient graph structures
- Contribute to tiered storage architecture for vector data across hot/warm/cold tiers to improve cost efficiency
Implement memory-optimized approaches such as MMAP-based graph loading and partial graph loading to reduce working set size
- Collaborate with applied scientists on novel algorithmic approaches to vector search optimization
- Participate in open-source community contributions, code reviews, and technical design discussions