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. 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.
Responsibilities
- 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.
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. Experience with large open-source and enterprise codebases.
- Proven experience in profiling and optimizing distributed systems. 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.
Conditions
- Compensation for this role is in the form of base salary. This role does not have a variable compensation component.
- The typical starting salary range for new hires in this role is $133,100–$210,600 USD. In select locations (including Seattle WA, Los Angeles CA, the San Francisco Bay Area CA, and the New York City Metro Area), an alternate range may apply: $159,900–$252,900 USD.
- In addition to cash compensation, this role is currently eligible to participate in Elastic's stock program.
- Total rewards package includes a company-matched 401k with dollar-for-dollar matching up to 6% of eligible earnings, along with a range of other benefits offered with a holistic emphasis on employee well-being.