Senior Engineer, Competitive Benchmarking & Performance

Elasticsearch powers search, observability, and AI retrieval (RAG) for the world's largest organizations. We are seeking a Senior Software Engineer to lead competitive benchmarking initiatives within the Elasticsearch Performance team. 

You will develop representative workloads, automate reproducible experiments, and investigate how Elasticsearch compares with alternative systems. Your work will identify performance strengths, gaps, and tradeoffs, guide engineering improvements, and help customers make informed technical decisions.

This is a hands-on engineering role focused on building benchmarks and investigating performance, with technical publishing as a shared responsibility alongside engineering and content partners.

A typical investigation might ask how Elasticsearch and an alternative system compare under concurrent ingestion and hybrid search, at comparable retrieval quality and cost, and which engineering changes would improve the outcome. This role offers the opportunity to connect deep performance analysis with product improvements and public technical work.

• Own prioritized competitive benchmarking initiatives across search, vector and hybrid retrieval, and analytical and observability workloads.
• Build and maintain automated benchmark harnesses, datasets, query sets, and load profiles that reflect real-world usage, including concurrent ingestion and querying.
• Design fair comparisons with documented system versions, configurations, tuning decisions, resource budgets, and functional differences.
• Evaluate latency distributions, throughput, resource consumption, and cost efficiency. For retrieval workloads, measure performance at comparable recall or relevance targets.
• Adapt and extend workload-appropriate tools such as Elasticsearch Rally, ClickBench, and vector search benchmark frameworks.
• Investigate performance differences using profiling, system metrics, and controlled experiments; collaborate with engineering teams to identify causes and validate improvements.
• Evaluate relevant self-managed and managed cloud deployments, including serverless offerings, accounting for scaling behavior and service constraints.
• Work with engineering and technical content partners to publish reproducible benchmark artifacts and clear technical reports that explain methodology, findings, limitations, and tradeoffs.
• Partner with engineering and product management to prioritize benchmark coverage and translate findings into actionable product improvements.

• Solid software engineering skills, with 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.
• Depth in at least one relevant area (search, vector retrieval, analytical databases, or observability systems) and the ability to learn unfamiliar systems.
• Experience designing representative datasets and workloads, including query patterns, concurrency, and ingestion behavior.
• Sound experimental judgment, including repeated measurements, variability analysis, warm-up and cache effects, and the distinction between meaningful changes and measurement noise.
• Experience building reproducible test environments and documenting configurations, procedures, and results.
• Clear technical writing and communication skills, with the ability to explain complex findings and their limitations.
• Solid collaboration skills and the ability to turn performance evidence into applicable engineering recommendations.

• Experience with Elasticsearch, Lucene, Rally, ClickBench, or vector search benchmark frameworks.
• Experience measuring retrieval quality alongside latency, throughput, and resource usage.
• Familiarity with cloud performance and cost analysis, including autoscaling and serverless systems.
• Public technical writing, open-source contributions, or published benchmark studies.