Top Tech Jobs & Startup Jobs in Dublin

Lead development of Elasticsearch search relevance and vector search capabilities. Design and implement new search features, algorithms, and data structures; fix complex bugs; improve performance and maintainability; and contribute to technical direction. Collaborate asynchronously with globally distributed engineers and open-source contributors, write modern Java, review code, and guide initiatives from design through delivery.

Lead development of Elasticsearch search and vector-search capabilities, designing algorithms and data structures, implementing Java features, fixing complex bugs, improving relevance and performance, and contributing to technical direction. Collaborate asynchronously with globally distributed engineers and open-source contributors, review code, triage issues, and drive initiatives from design through completion.

Lead architectural and code-level performance engineering for Elasticsearch, from design through production. Develop performance models, profile distributed systems, identify bottlenecks, optimize stateful and serverless architectures, and build benchmarking and regression-detection automation. Drive AI-assisted optimization tooling, collaborate across teams, and mentor engineers. Focus areas include Java and JVM performance, concurrency, distributed systems, logging, metrics, vector search, ES|QL, storage engines, and scalable search infrastructure.

Build and evolve the Elastic MCP server and tooling (primarily in Go), create a portable platform for agent skills, design third-party AI ecosystem integrations, enhance the Elastic CLI, and develop/maintain libraries and integrations (including Rust). Drive correctness via evals, collaborate with product and external partners, and prioritize developer experience and product thinking across fast-changing, ambiguous work.

Design and improve Elasticsearch distributed-systems components for consistent, concurrent indexing and cluster coordination. Scale shards, nodes, and petabyte datasets, diagnose performance and concurrency issues, support operations/support teams, and own end-to-end projects including technical design and cross-team development.

Design, implement, and optimize Elasticsearch core storage and query engine for observability workloads. Lead architecture, resolve performance bottlenecks, improve storage/indexing/query execution, mentor engineers, drive cross-team initiatives, collaborate with global community, and write idiomatic modern Java.

Design, implement, and scale Elasticsearch distributed systems: improve concurrent indexing, maintain cluster coordination, optimize performance at petabyte scale, debug concurrency and performance issues, and lead cross-engineer projects.

Full‑stack engineer on the self‑service cloud growth team building onboarding and trial experiences. Design and implement scalable frontend and backend features, APIs, agentic interfaces, and data evolution strategies. Collaborate with PMs, designers, and solution teams to integrate cloud and Kibana flows and operationalize software in production.