Staff+ Software Engineer, VectorDB

Anthropic•Seattle, WA

10h•$320,000 - $485,000•Hybrid

About The Position

Anthropic is hiring a Staff+ Software Engineer to build and evolve the vector search infrastructure behind Claude. The role is part of the Databases organization and focuses on the architecture, performance, scalability, cost, and production operation of the vector database layer serving retrieval workloads, including web search, conversational memory, and reinforcement learning. The engineer will work closely with the Search Platform and Memory & Search teams. This is a hands-on technical leadership role involving implementation as well as architectural decisions for a foundational system whose core design is still taking shape.

Requirements

Direct experience building a vector database or approximate nearest-neighbor search system that ran in production at scale.
Deep knowledge of ANN index structures, including 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 skills, including experience driving alignment across teams with different priorities.
Interest in AI safety and Anthropic's mission.
A bachelor's degree or an equivalent combination of education, training, and/or experience.
A field of study relevant to the role, demonstrated through coursework, training, or professional experience.

Nice To Haves

Experience with object-storage-backed or disaggregated compute and storage database designs.
Experience building or operating full-text search engines, including inverted indexes and 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.

Responsibilities

Own the vector database layer, including index structures, storage layout, query execution, and serving.
Ensure production readiness through benchmarking against exact search, capacity planning, workload onboarding, and operational tooling.
Manage trade-offs among recall, latency, and cost, including quantization, filtered search, index build and refresh strategies, and scaling compute and storage.
Increase system throughput by an order of magnitude as research and training demand grows, and identify whether bottlenecks stem from architecture, hardware, or engineering bandwidth.
Extend the retrieval stack toward hybrid retrieval, including lexical search and fused ranking alongside vector search.
Define evaluation methodology, including recall targets, latency objectives, cost per query, and regression detection.
Set technical direction for a growing team, lead design reviews, make architectural decisions, and mentor engineers.
Share on-call responsibility for systems owned by the team.