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Software Development Engineer - OpenSearch, VectorSearch, OpenSearch

Job in Bellevue, King County, Washington, 98009, USA
Listing for: Amazon
Full Time position
Listed on 2026-09-03
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Software Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Position: Software Development Engineer - Amazon Open Search, VectorSearch, Open Search

Software Development Engineer (SDE II) - Vector Search Team

The Open Search 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 Open Search 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) 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 Open Search, 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
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