Lead Performance Engineer, Distributed Systems & AI

Socket.dev; Town of Poland (NY); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Health coverage

Flexible schedules

Generous vacation days

Donation matching

Volunteer hours

Parental leave

Job summary

Elastic is seeking a Principal Software Engineer to join the Elasticsearch Performance team in New York. You will lead architectural and code-level performance initiatives, drive optimization strategies, and partner with cross-functional teams to unlock the full potential of our software.

You will mentor engineers, develop performance models, and build AI-assisted optimization workflows to streamline profiling and benchmarking in distributed environments.

Qualifications

• Deep knowledge of Java internals and JVM memory management.
• Experience with concurrency models and writing high-performance, thread-safe code.
• Proven profiling and optimization of distributed systems using benchmarking tools.

Responsibilities

• Own core performance engineering initiatives from architecture to production.
• Develop performance models and methodologies for distributed systems.
• Drive optimization strategies for scalability and predictability across environments.
• Profile and analyze system behavior to identify bottlenecks in logging, metrics, and vector search.
• Ensure robust benchmarks and regression detection for stateful and serverless architectures.
• Mentor engineers and promote performance-aware development.

Skills

Java internals

JVM memory management

Concurrency

High-performance coding

Thread-safety

Distributed systems profiling

Tools

Flamegraphs

JMH

Rally

AI tooling

Job description

Elastic is seeking a Principal Software Engineer to join the Elasticsearch Performance team in New York. You will lead architectural and code-level performance initiatives, drive optimization strategies, and partner with cross-functional teams to unlock the full potential of our software.

You will mentor engineers, develop performance models, and build AI-assisted optimization workflows to streamline profiling and benchmarking in distributed environments.