Drimva

Elastic

Senior Engineer, Competitive Benchmarking & Performance

$133K–$253K

United States +1 · Senior · Full-time · Posted Oct 21, 2026

Location
United States
Canada
Visa sponsorship
An export license may be required as a condition of employment for applicants located in or nationals of the listed sanctioned countries and regions.

About the job

Leads hands-on competitive performance benchmarking of Elasticsearch against alternative systems, producing evidence and recommendations that guide product improvements and technical reports.

What you'll do

  • Build and maintain reproducible benchmark workloads, datasets, and automation for search, retrieval, analytics, and observability.
  • Analyze performance, resource use, and cost through controlled experiments; collaborate with engineering teams to investigate causes and validate improvements.
  • Publish benchmark artifacts and reports with engineering and technical content partners.

What they ask for

  • Software engineering experience building maintainable automation and analysis tools in Python or another relevant programming language.
  • Hands-on experience evaluating and diagnosing performance in distributed systems, databases, or search infrastructure.
  • Experience designing representative datasets and workloads and building reproducible test environments.
  • Strong experimental judgment, technical writing, communication, and collaboration skills.

Nice to have

  • Experience with Elasticsearch, Lucene, Rally, ClickBench, or vector search benchmark frameworks.
  • Experience measuring retrieval quality or analyzing cloud performance and cost, including autoscaling and serverless systems; public technical writing, open-source contributions, or published benchmark studies.

Pay

$133,100 — $210,600 USD (base salary); $159,900 — $252,900 USD (base salary)

Tools

Python, Elasticsearch, Lucene, Rally, ClickBench

AI focus

Benchmarks search, vector and hybrid retrieval workloads, including AI retrieval (RAG); measures retrieval performance at comparable recall or relevance targets.

Model-written summary · the original posting is authoritative

First seen Oct 27, 2026 · Last verified Nov 5, 2026 (UTC)