Benchmark Engineer at Qdrant

Germany

<p>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.</p> <p><strong>The Role</strong></p> <p>We’re looking for a <strong>Benchmark Engineer</strong> 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.</p> <p>This role sits at the intersection of <strong>engineering, performance, and developer experience</strong>.</p> <h2 id="tasks">Tasks</h2> <p><strong>What You’ll Do</strong></p> <ul> <li>Design and maintain <strong>reproducible benchmarks</strong> for vector search, indexing, filtering, and distributed workloads</li> <li>Evaluate performance across different dimensions: latency, throughput, recall, memory usage, and cost</li> <li>Compare Qdrant against alternative solutions in a <strong>fair, transparent, and technically sound</strong> way</li> <li>Build and maintain benchmarking tooling, datasets, and automation (CI, dashboards, reports)</li> <li>Collaborate closely with core engineers to identify regressions, bottlenecks, and optimization opportunities</li> <li>Help translate benchmark results into <strong>clear narratives</strong> for docs, blog posts, and talks</li> <li>Ensure benchmarks reflect <strong>real-world user workloads</strong>, not just synthetic best cases</li> </ul> <h2 id="requirements">Requirements</h2> <p><strong>What We’re Looking For</strong></p> <ul> <li>Strong software engineering background (Rust, Python, Go, or similar)</li> <li>Solid understanding of <strong>databases, distributed systems, or search engines</strong></li> <li>Experience with <strong>performance testing, profiling, and benchmarking</strong></li> <li>Ability to reason about trade-offs (speed vs accuracy, memory vs latency, etc.)</li> <li>Comfort working with large datasets and automation pipelines</li> <li>Clear communication skills — you can explain numbers and their implications</li> </ul> <p><strong>Nice to Have</strong></p> <ul> <li>Experience with vector search, ANN algorithms, or ML infrastructure</li> <li>Familiarity with cloud environments and containerized workloads</li> <li>Experience contributing to open-source projects</li> <li>Knowledge of observability tools and performance profiling</li> </ul> <h2 id="benefits">Benefits</h2> <p><strong>Why Join Qdrant</strong></p> <ul> <li>Work on <strong>core infrastructure for modern AI systems</strong></li> <li>Open-source, engineering-driven culture</li> <li>Fully remote team with flexible working hours</li> <li>High ownership, real impact, and technical depth</li> <li>Opportunity to shape how the industry evaluates vector databases</li> </ul> <p>Recruiting Agencies and Headhunters, please only via 𝗵𝘁𝘁𝗽𝘀://𝗵𝗶𝗿𝗲𝗯𝘂𝗳𝗳𝗲𝗿.𝗰𝗼𝗺?ref=qdrant</p>