About the company
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.
Responsibilities
- 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.
Requirements
- Deep knowledge of Java internals and JVM memory management.
- Understanding of concurrency models, ability to write high-performance, thread-safe, and lock-free code.
- Proven experience in profiling and optimizing distributed systems, including benchmarking tools (e.g., flamegraphs, 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 across functions and teams, acting as a force multiplier for performance engineering.
- Ability to work autonomously, drive decisions and result in a distributed team by leveraging asynchronous, direct, and transparent communication.
Conditions
- Compensation is in the form of base salary; this role does not have a variable compensation component.
- Typical starting salary range: $159,800 — $252,800 USD.
- In select locations (Seattle WA, Los Angeles CA, San Francisco Bay Area CA, New York City Metro Area): $191,900 — $303,500 USD.
- Eligible to participate in Elastic's stock program.
- Company-matched 401k with dollar-for-dollar matching up to 6% of eligible earnings.
- Range of other benefits with a holistic emphasis on employee well-being.