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