Senior Software Development Engineer, DynamoDB Search and Indexing
Amazon DynamoDB is a serverless, distributed NoSQL database that delivers single-digit millisecond performance at virtually any scale. The DynamoDB Search and Indexing organization is building native search capabilities that allow customers to perform vector similarity search, full-text search, and hybrid search across their operational data.
We are looking for a Senior Software Development Engineer to help design and build the distributed indexing, retrieval, and ranking infrastructure behind these capabilities. You will work on problems at the intersection of databases, distributed systems, information retrieval, and machine-learning infrastructure.
The role includes designing vector and text-indexing architectures that operate across partitioned datasets; integrating approximate nearest-neighbor algorithms and inverted indexes with DynamoDB’s storage and query systems; and building hybrid retrieval that combines semantic similarity, lexical relevance, and structured filtering.
You will help determine how search indexes are created, maintained, rebuilt, rebalanced, and recovered while the underlying DynamoDB data continues to change. You will also shape query planning, distributed execution, relevance ranking, and result aggregation while delivering predictable performance across latency, throughput, recall, freshness, availability, and cost.
This is an opportunity to shape new DynamoDB capabilities from foundational architecture through customer launch and operation at DynamoDB scale.
Key job responsibilities
* Design and implement distributed vector, full-text, and hybrid-search capabilities for DynamoDB.
* Build vector-indexing and approximate nearest-neighbor retrieval mechanisms for high-dimensional embeddings.
* Develop full-text indexing and retrieval capabilities, including tokenization, text analysis, inverted indexes, relevance scoring, and phrase or term-based search.
* Build hybrid retrieval and ranking that combines vector similarity, lexical relevance, and structured attribute filters.
* Evaluate ranking and result-fusion techniques for combining semantic and lexical results, including score normalization, rank fusion, and reranking.
* Design distributed query execution, including partition selection, query fan-out, local retrieval, top-k result merging, filtering, and aggregation.
* Build index lifecycle mechanisms covering creation, backfill, incremental updates, compaction, schema or configuration changes, rebalancing, recovery, and deletion.
* Ensure that indexes remain sufficiently synchronized with changes to the underlying DynamoDB data and define clear search-freshness and consistency semantics.
* Integrate search indexes with DynamoDB’s distributed storage, partitioning, replication, and query-processing systems.
* Optimize tradeoffs among search quality, recall, relevance, query latency, ingestion throughput, index freshness, memory usage, storage amplification, and infrastructure cost.
* Develop benchmarking and correctness frameworks for measuring retrieval quality, recall, ranking relevance, performance, scalability, and behavior during failures.
* Design systems that remain available and operationally manageable during node failures, partition movement, traffic spikes, index rebuilds, and software deployments.
* Work closely with database, storage, search, and machine-learning engineers to define interfaces and end-to-end system architecture.
* Translate ambiguous customer requirements into technical designs and deliver production-quality capabilities.
* Provide technical leadership through design reviews, implementation guidance, operational-readiness reviews, and mentoring.
* Operate the service in production and use operational data to improve reliability, performance, search quality, and customer experience.
Basic qualifications
* 5+ years of non-internship professional software development experience.
* 5+ years of programming with at least one software programming language.
* 5+ years of leading the design or architecture of new and existing systems, including design patterns, reliability, and scaling.
* Experience building distributed systems, databases, search infrastructure, storage engines, or other high-performance data platforms.
* Strong understanding of algorithms, data structures, concurrency, and system performance.
* Experience taking complex systems from design through implementation, launch, and production operation.
Preferred qualifications
* Experience with vector search, full-text search, hybrid search, approximate nearest-neighbor search, or large-scale information retrieval.
* Knowledge of vector-indexing techniques such as HNSW, IVF, clustering, product quantization, or comparable approaches.
* Knowledge of full-text retrieval concepts such as inverted indexes, analyzers, tokenization, BM25, relevance scoring, query parsing, and ranking.
* Experience combining lexical, semantic, and structured retrieval through rank fusion, score normalization, filtering, or reranking.
* Experience building distributed query-planning and execution systems, including query fan-out and top-k result aggregation.
* Experience integrating secondary indexes with database storage and query systems.
* Experience with storage-engine concepts such as LSM trees, write-ahead logging, compaction, caching, snapshots, and recovery.
* Experience designing partitioned or sharded systems with replication, online rebalancing, and failure recovery.
* Experience developing performance and quality benchmarks that evaluate latency, throughput, recall, relevance, freshness, memory, and storage tradeoffs.
* Experience with Java, Rust, or another systems-programming language.
* Master’s degree or PhD in computer science, engineering, mathematics, or a related field.