About this role
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.
The Role
We’re looking for a Benchmark Engineer to own and evolve how we measure, validate, and communicate Qdrant’s performance. You’ll design realistic benchmarks, build tooling around them, and transform raw numbers into actionable insights that inform product decisions, documentation, and user trust.
This role sits at the intersection of engineering, performance, and developer experience.
Tasks What You’ll Do
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 What We’re Looking For
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
Nice to Have
Experience with vector search, ANN algorithms, or ML infrastructure Familiarity with cloud environments and containerized workloads Experience contributing to open-source projects Knowledge of observability tools and performance profiling
Benefits Why Join Qdrant
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
Recruiting Agencies and Headhunters, please only via 𝗵𝘁𝘁𝗽𝘀://𝗵𝗶𝗿𝗲𝗯𝘂𝗳𝗳𝗲𝗿.𝗰𝗼𝗺?ref=qdrant