Senior Software Engineer - Performance Tuning - Elasticsearch

What is The Role

Elasticsearch powers search, observability, and AI retrieval (RAG) for the world's largest organizations. We are seeking a Senior Software Engineer to join the Elasticsearch Performance team. In this role, you will contribute to performance engineering initiatives through high-quality code and technical analysis. Your goal is to help improve the optimization and predictability of Elasticsearch performance, collaborating with area-specific teams to enhance our software.

What You Will Be Doing

• Contributing to core performance engineering initiatives from development to production, focusing on the delivery of impactful optimizations. Executing technical designs and plans for architectural and code-level performance improvements.
• Implementing foundational performance models and methodologies for complex, distributed systems.
• Supporting optimization strategies to ensure Elasticsearch remains performant, predictable, and scalable in diverse environments.
• Profiling and analyzing system behavior to identify bottlenecks in logging, metrics, vector search, and ES|QL.
• Ensuring robust performance benchmarks and regression detection for both stateful and stateless (Serverless) architectures.
• Collaborating with peers across the team to apply performance-focused development practices into new features.
• Contributing to automation efforts by building AI-assisted optimization harnesses that streamline profiling, hypothesis testing, and benchmarking.
• Providing technical guidance and peer reviews to other engineers, fostering a culture of technical excellence.

What You Bring

• Deep knowledge of Java internals and JVM memory management. You understand how concurrency models work. You can write code that is high-performance, thread-safe, and lock-free. This experience includes working with large open-source and enterprise codebases.
• Proven experience in profiling and optimizing distributed systems. This includes deep experience with benchmarking tools (e.g., JMH, Rally), identifying performance regressions, and implementing algorithmic or hardware-aware optimizations.
• Solid comprehension of distributed systems architecture, including partition tolerance, cluster state propagation, and scaling challenges in large-scale data stores.
• Proven track record of using AI or advanced tooling to accelerate optimization, debug complex performance issues, and automate benchmarking workflows.
• Ability to collaborate effectively within a team environment, contributing to the success of performance engineering goals.
• Ability to collaborate across functions and teams and seamlessly transition between different projects, codebases, or teams based on business priorities.
• Ability to work autonomously, drive decisions, and result in a distributed team by leveraging asynchronous, direct, and transparent communication.

Bonus Points

• Experience integrating high-performance native libraries (e.g., C++, Rust, SIMD-accelerated code) into Java applications.
• Deep knowledge of modern storage engine performance, index modes, or vector search optimizations.
• Experience defining and managing Performance SLAs and success criteria for distributed systems.
• Experience working on the internals of a large-scale data store or search engine.
• Experience working on the internals of a data store or search engine.