
Qdrant is an open-source vector database that enables AI applications to perform high-performance similarity search over high-dimensional embeddings. It implements a production-ready vector search…

Qdrant is an open-source vector database that enables AI applications to perform high-performance similarity search over high-dimensional embeddings. It implements a production-ready vector search…
What: Open-source Rust-native vector similarity search engine and vector database
Founded: 2021 (Berlin)
Product: Self-managed deployments and Qdrant Cloud with features for hybrid search, multivector support, real-time indexing, and advanced metadata filtering
Funding: Raised multiple rounds including $7.5M seed, $28M Series A, and $50M Series B
Tech focus: High-performance vector search, custom storage engine (Gridstore), SIMD optimizations, HNSW, quantization
Unstructured data retrieval and similarity search for AI applications (semantic search, RAG, agents, recommendations).
2021
Software Development
7.5M
Seed reported in April 2023; participation from 42CAP, IBB Ventures and angel investors
28M
Series A announced January 23, 2024; existing investors participated
50M
Series B announced by the company; participation from Bosch Ventures, Unusual Ventures, Spark Capital and 42CAP
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This role sits at the intersection of infrastructure, pricing, product, and finance, and exists to make sure our cloud cost structure scales with customer behavior, feature growth, and revenue.
This is not just a cost optimization role. It is a unit economics role. You will help ensure that every new feature, workload, and customer segment contributes to healthy gross margins and predictable growth.
What you’ll do
What you’ll bring
Nice to have
Shape the future of AI infrastructure
Candidates, please submit a short non-AI-generated cover letter and explain why you think you are a great fit for this role.
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