Benchmark Engineer

Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations. Trusted by global leaders like Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom, we’re building the retrieval infrastructure layer for modern AI. Recently raising $50M in Series B funding, we are growing rapidly and committed to transforming how AI understands and interacts with data. As a remote-first company, we believe diverse backgrounds, perspectives, and experiences fuel innovation. Here, you’ll own meaningful work, tackle challenges, and grow alongside passionate individuals dedicated to shaping the future of AI. 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. What you will own 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. Who you are 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. Why join us A remote-first, international team working on cutting-edge AI infrastructure. A competitive salary with additional perks. Flexible working hours and async-friendly culture. High ownership and real impact. Open-source, engineering-driven culture. Choose your own laptop equipment. For US-based candidates, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy. Qdrant is an equal-opportunity employer. We believe the best ideas come from diverse teams, and we actively welcome applicants from all backgrounds. If this role excites you but you don't check every single box, we'd still love to hear from you! We don't want to miss out on great people because of a checklist. Come build with us! For information on how we handle your personal data, please refer to our Recruitment Privacy Policy Apply To This Job