Join the globally distributed Elasticsearch Search Relevance team to design and implement new search features, improve vector search capabilities, and resolve complex bugs. The role emphasizes Java development, work with vector similarity algorithms (e.g., HNSW, IVF), collaboration on open-source components like Lucene/Elasticsearch, and operating at scale in distributed systems.

Work on Elasticsearch's Search Relevance team to design and implement new search features, improve search relevance, and contribute to Lucene. The role requires writing modern Java, using and implementing vector-search algorithms (e.g., HNSW, IVF), collaborating with a distributed team, triaging community issues, and delivering scalable, performant search functionality.

Join the globally distributed Elasticsearch Search Relevance team to design and implement new search features, improve search relevance, and work on vector search capabilities. The role requires strong core Java skills, experience with vector similarity (HNSW, IVF) or vector databases, familiarity with Elasticsearch/Lucene, and working on large-scale search platforms. Compensation range listed in CAD.

Seeking a Senior Software Engineer to lead initiatives in Elasticsearch search relevance and vector search, design and implement search features, improve performance, and fix bugs. The role emphasizes modern Java development, experience with search/vector databases (HNSW, IVF), working in a distributed, asynchronous engineering environment, and collaboration on open source projects.

Role to design and implement new search features, improve search relevance (including work touching Lucene), contribute to vector search capabilities (HNSW, IVF), fix bugs, and collaborate with a globally distributed engineering team. Compensation range listed for Poland: 307,800 — 486,900 PLN (base salary).

Join the globally distributed Elasticsearch Search Relevance team to lead design and implementation of search features and relevance improvements (including contributions to Lucene and vector search). The role requires strong Java skills, experience with search/vector databases and algorithms (HNSW, IVF), working in distributed teams, open source collaboration, and improving search performance and reliability. Listed salary range: 791,300–1,251,700 NOK per year.

Join the globally distributed Elasticsearch Search Relevance team to design and implement new search features, improve relevance (including contributions to Lucene), and work on vector search capabilities. The role requires strong Java skills, experience with search or vector databases (HNSW, IVF, etc.), algorithm and data-structure design, and collaborative development in distributed/open-source environments.

Join the Elasticsearch Search Relevance team to design and implement search features (including vector search using HNSW/IVF), improve Lucene integration, write modern Java, triage and handle community issues, and collaborate with a globally distributed engineering team on open source development.

Join the globally distributed Elasticsearch Search Relevance team to design and implement new search features, improve relevance (including contributions to Lucene), work on vector search algorithms (HNSW, IVF), and build high-performance Java code. The role involves collaborating with community members and distributed engineers, debugging complex systems, and contributing to open source.

Elastic is hiring a Senior Software Engineer for the Elasticsearch - Search Relevance team to lead initiatives that improve search speed and relevance, contribute new search features and fixes (including work touching Lucene), and collaborate with a globally distributed engineering team. The role emphasizes expertise in search/vector databases and algorithms (e.g., HNSW, IVF), strong core Java skills, open-source collaboration, and autonomous, asynchronous distributed-team working.

Join the globally distributed Elasticsearch Search Relevance team to lead development of search features, improve Lucene, and work on vector search capabilities. Responsibilities include designing and implementing algorithms and data structures, fixing complex bugs, collaborating across a distributed team, and writing idiomatic modern Java. Required experience includes search or vector databases and familiarity with algorithms such as HNSW or IVF; experience with data storage technologies (Elasticsearch, Solr, PostgreSQL, MongoDB, Cassandra) and using AI tools to aid development is highlighted.

Work as a senior engineer on Elasticsearch's Search Relevance team to design and implement search features and algorithms (including vector search and work with HNSW/IVF), contribute to Lucene, fix bugs, and collaborate with a distributed, open-source-focused team. Role emphasizes modern Java development, system-level and algorithmic work, and collaboration across distributed teams. Compensation includes a listed CAD annual salary range, benefits, and participation in stock program.

Elastic is hiring a Senior Salesforce Engineer to own end-to-end design, development, testing, deployment, and support of Salesforce and integration solutions. The role involves Lightning Web Components, Apex, SOQL/SOSL, Flows, REST APIs, event-driven patterns, CI/CD, and use of AI-assisted coding tools (Cursor, Claude Code, Codex, Copilot). The role expects 5+ years of full-stack engineering experience. Typical starting salary range: $94,300–$149,200 USD per year.

Full-time Principal Software Engineer role contributing to Elasticsearch core search infrastructure (indexing, retrieval, relevance). Responsibilities include defining technical vision, researching search/data-structure advances, leading and contributing to core features, collaborating with product and partner teams, mentoring engineers, and working across distributed teams. Requires deep experience with search technologies (Elasticsearch, Lucene, Solr), core Java, data structures, concurrency, and large-scale distributed systems.

Lead performance engineering initiatives for Elasticsearch, owning technical design and execution of optimizations, building performance models and benchmarks (stateful and serverless), profiling and identifying bottlenecks across logging, metrics, vector search and ES|QL, and driving automation including AI-assisted optimization harnesses. Requires deep Java/JVM knowledge, experience profiling and benchmarking distributed systems, and familiarity integrating native libraries and storage/index performance.

Lead and deliver high-impact performance engineering initiatives for Elasticsearch, including architecture and code improvements, profiling and benchmarking (flamegraphs, JMH, Rally), performance modeling, regression detection, and automation (AI-assisted optimization). Requires deep Java/JVM knowledge, experience with distributed systems and performance tuning, and mentoring other engineers.

Lead high-impact performance initiatives across architecture and code, develop performance models and methodologies for distributed systems, profile and benchmark components (logging, metrics, vector search, ES|QL), drive automation and AI-assisted optimization tooling, and mentor engineers to embed performance-first thinking across the company. Requires deep Java/JVM and distributed systems expertise and experience with profiling and benchmarking tools.

Lead architectural and code-level performance engineering initiatives for Elasticsearch, including profiling, benchmarking, developing performance models, and driving optimizations across distributed systems. Role emphasizes JVM/Java internals, concurrency, benchmarking tools (flamegraphs, JMH, Rally), vector search and serverless performance, and automation/AI-assisted optimization. Listed typical starting salary range: 369,400–584,300 PLN per year.

Lead high-impact performance engineering initiatives for Elasticsearch, including architectural design, profiling and benchmarking (stateful and serverless), developing performance models, and driving optimizations across logging, metrics, vector search and ES|QL. Requires deep Java/JVM and distributed systems expertise, experience with benchmarking tools (flamegraphs, JMH, Rally), and using AI/automation to accelerate optimization. Role based in Portugal.

Lead architectural and code-level performance engineering for Elasticsearch: develop performance models, profile and benchmark distributed systems (stateful and serverless), drive optimizations across logging, metrics, vector search and ES|QL, build AI-assisted benchmarking tools, and mentor engineers. Requires deep Java/JVM, concurrency, profiling, and distributed systems experience.