Principal Software Engineer - Performance Tuning - Elasticsearch

Compensation for this role is in the form of base salary. This role does not have a variable compensation component.

At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets.

The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position.

The typical starting salary range for this role is:

€80.400-€127.200 EUR

• You have 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.
• You have proven experience in profiling and optimizing distributed systems. This includes deep experience with benchmarking tools (e.g.,flamegraphs, JMH, Rally), identifying performance regressions, and implementing algorithmic or hardware-aware optimizations.
• You have a solid comprehension of distributed systems architecture, including partition tolerance, cluster state propagation, and scaling challenges in large-scale data stores.
• You have a proven track record of using AI or advanced tooling to accelerate optimization, debug complex performance issues, and automate benchmarking workflows.
• You possess the ability to collaborate across functions and teams, acting as a force multiplier for performance engineering across the organization.
• You possess the ability to collaborate across functions and teams and seamlessly transition between different projects, codebases, or teams based on business priorities
• You can work autonomously, drive decisions and result in a distributed team by leveraging asynchronous, direct, and transparent communication.

• 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.

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale - unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data - securing and protecting private information more effectively - Elastic's complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.

Elasticsearch powers search, observability, and AI retrieval (RAG) for the world's largest organizations. We are seeking a Principal Software Engineer to join the Elasticsearch Performance team. In this role, you will lead architectural and code-level performance engineering initiatives. Your goal is to drive the continuous optimization and predictability of Elasticsearch performance, partnering with area-specific teams to unlock the full potential of our software.

• Owning core performance engineering initiatives from architecture to production, focusing on the delivery of high-impact optimizations. Leading the technical design, plan, and execution for major architectural and code-level performance improvements.
• Developing foundational performance models and methodologies for complex, distributed systems.
• Driving 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 across the company to embed performance-first thinking into new features from the outset.
• Drive automation efforts by designing and building AI-assisted optimization harnesses that streamline profiling, hypothesis testing, and benchmarking.
• Mentoring and coaching other engineers, fostering a culture of technical excellence and performance-aware development.