Staff+ Software Engineer (VectorDB)

Join Anthropic, a leading AI safety and research company, as a Staff+ Software Engineer. In this foundational role, you will be responsible for the vector database layer that serves Claude's retrieval workloads. You will work closely with the Search Platform team and the Memory & Search team, and will have a significant impact on the architecture, performance, scalability, and cost of the system. This is a hands-on position that requires experience in building vector or search systems that have run in production at scale.

• Posséder le cœur de la couche de base de données vectorielle d'Anthropic : structures d'index, disposition de stockage, exécution de requêtes et couche de service.
• Assurer la préparation à la production - évaluation par rapport à la recherche exacte, planification de la capacité, intégration de nouvelles charges de travail.
• Déterminer la direction technique pour une équipe en croissance - diriger les revues de conception, prendre des décisions architecturales sur lesquelles les autres s'appuient.

• Experience with search relevance and ranking
• Strong systems programming skills in a language such as Rust, C++ or Go, including performance-critical and memory-intensive code
• Interest in AI safety and Anthropic's mission
• Direct experience building a vector database or approximate nearest-neighbor search system that ran in production at scale
• Deep knowledge of ANN index structures (graph-based, clustering-based or disk-resident approaches), vector quantization and filtered search
• Experience designing and operating large-scale distributed storage or serving systems with strict latency and reliability requirements
• A track record of independently scoping and delivering ambiguous, multi-quarter technical projects
• Strong written and verbal communication, with experience driving alignment across teams with different priorities
• Prior experience as a technical lead on a database or search infrastructure team
• Experience using GPUs or other accelerators for index build or search
• Familiarity with embedding models and the retrieval demands of LLM training and inference
• Experience building or operating full-text search engines (inverted indexes, BM25) or hybrid retrieval systems
• Experience with object-storage-backed or disaggregated compute and storage database designs