Staff+ Software Engineer, VectorDB

About the role
Anthropic is looking for a Staff+ Software Engineer to work on the vector search infrastructure behind Claude. Retrieval is how Claude learns about the world. Web search across claude.ai, the API and agentic surfaces, memory across conversations, and the reinforcement learning workloads that train our next models all depend on fast, cheap, high-recall nearest-neighbor search over billions of vectors.

You'll join a small team responsible for the vector database layer that serves Claude's retrieval workloads: its architecture, performance, scalability and cost. This is a foundational role. The team is small, core architectural decisions are still open, and the engineers who join now will shape how the system evolves.

The role sits in our Databases organization and works day to day with the Search Platform team, which owns web search indexing, retrieval and serving, and the Memory & Search team, which owns search and memory inside claude.ai.

We're looking for someone who has built vector or search systems that ran in production, at scale, with real users and a real on-call. You'll be hands-on in the code, and you'll also be the person deciding how the pieces fit together.

Key responsibilities
Own the core of Anthropic's vector database layer: index structures, storage layout, query execution and the serving layer
Own production readiness - benchmarking against exact search, capacity planning, onboarding new workloads, and the operational tooling to run it at scale
Drive the recall, latency and cost trade-offs - quantization, filtered search, index build and refresh strategy, and how compute and storage are separated and scaled
Scale throughput by an order of magnitude as research and training demand grows, and work out whether the bottleneck is architecture, hardware or engineering bandwidth
Extend the retrieval stack toward hybrid retrieval - lexical search and fused ranking alongside vector search
Set the evaluation methodology for the system - recall targets, latency objectives, cost per query, and the regression detection that keeps them honest
Set technical direction for a growing team - lead design reviews, make architectural decisions others build on, and mentor the engineers joining the effort
Share the on-call for the systems you own

Minimum qualifications
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 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
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
Interest in AI safety and Anthropic's mission

Preferred qualifications
Experience with object-storage-backed or disaggregated compute and storage database designs
Experience building or operating full-text search engines (inverted indexes, BM25) or hybrid retrieval systems
Familiarity with embedding models and the retrieval demands of LLM training and inference
Experience using GPUs or other accelerators for index build or search
Prior experience as a technical lead on a database or search infrastructure team