Job description

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. 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 The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Requirements

Must-haves

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
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

Nice-to-haves

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
The annual compensation range for this role is listed below.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
How we're different
Come work with us!

Application prep

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Benefits

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Parental Leave