Lead Java Architect - Distributed Systems (Serverless)

Elasticsearch B.V.

Northern (KY)

Hybrid

USD 160,000 - 253,000

Full time

14 days+
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Benefits offered by this job

Competitive pay
Healthcare
Flexible schedules
Vacation days
Stock option
Volunteer time
Parental leave

Job summary

Elastic, the Search AI Company, is seeking a Principal Software Engineer to join the Elasticsearch - Distributed Systems team and focus on scale, performance, and resilience in our Serverless offering. You will tackle deep distributed systems challenges in how nodes communicate, how data is indexed, allocated, and managed across nodes.

You will own end-to-end design and implementation, push the limits on shards and petabytes, and collaborate across teams to deliver high-impact, multi-machine

Qualifications

  • Deep background in distributed systems and consensus algorithms.
  • Strong Java proficiency and knowledge of data structures and concurrency.

Responsibilities

  • Design, build, and operate core pieces of the Serverless Elasticsearch offering.
  • Improve indexing across multiple machines to support concurrency and scale.
  • Maintain the cluster coordination system for high performance as nodes join/leave.
  • Push limits on shards, nodes, and petabytes handled by Elasticsearch.
  • Own end-to-end investigations and resolve performance or stability issues.
  • Collaborate with support engineers and other teams to deliver robust features.

Skills

Distributed systems
Java
Algorithms
Large-scale systems
Project ownership
Cross-team collaboration
AI-enabled development

Tools

Netty

Job description

Elastic, the Search AI Company, is seeking a Principal Software Engineer to join the Elasticsearch - Distributed Systems team and focus on scale, performance, and resilience in our Serverless offering. You will tackle deep distributed systems challenges in how nodes communicate, how data is indexed, allocated, and managed across nodes.

You will own end-to-end design and implementation, push the limits on shards and petabytes, and collaborate across teams to deliver high-impact, multi-machine

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