Staff+ Software Engineer, VectorDB

The shape of it

In the posting’s own words

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.

What it asks for · 8

• 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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• search18×
• experience8×
• systems8×
• vector8×
• retrieval7×
• database6×
• index4×
• scale4×
• storage4×
• cost3×
• engineers3×
• latency3×
• memory3×
• serving3×
• technical3×
• vector database3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

More open at Anthropic

• 3P Revenue Accounting & ReportingSan Francisco, CA | Seattle, WA
• Account Executive, AI NativeNew York City, NY; San Francisco, CA | New York City, NY
• Account Executive - DNBSingapore
• Account Executive, NonprofitSan Francisco, CA | New York City, NY
• Account Executive - Public Sector (ASEAN)Singapore
• Account Executive, StartupsDublin, IE