Benchmark Engineer - Remote

Overview

Qdrant is an open-source vector database built for high-performance similarity search and AI applications. We power production-grade semantic search, recommendation systems, and RAG pipelines for teams worldwide. As AI adoption accelerates, performance, correctness, and transparency matter more than ever — and that’s where you come in.

In Short

• Design and maintain reproducible benchmarks for vector search, indexing, filtering, and distributed workloads
• Evaluate performance across different dimensions: latency, throughput, recall, memory usage, and cost
• Compare Qdrant against alternative solutions in a fair, transparent, and technically sound way
• Build and maintain benchmarking tooling, datasets, and automation (CI, dashboards, reports)
• Collaborate closely with core engineers to identify regressions, bottlenecks, and optimization opportunities
• Help translate benchmark results into clear narratives for docs, blog posts, and talks
• Ensure benchmarks reflect real-world user workloads, not just synthetic best cases

Requirements

• Strong software engineering background (Rust, Python, Go, or similar)
• Solid understanding of databases, distributed systems, or search engines
• Experience with performance testing, profiling, and benchmarking
• Ability to reason about trade-offs (speed vs accuracy, memory vs latency, etc.)
• Comfort working with large datasets and automation pipelines
• Clear communication skills — you can explain numbers and their implications

Benefits

• Work on core infrastructure for modern AI systems
• Open-source, engineering-driven culture
• Fully remote team with flexible working hours
• High ownership, real impact, and technical depth
• Opportunity to shape how the industry evaluates vector databases