Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems. Backed by Accel, Bessemer and General Catalyst.
About the role
Every dollar Anthropic earns and every dollar it spends on compute has to reach the books correctly, and the volume grows with Claude usage. That data comes from across the company and from partners: usage and billing, compute, infrastructure assets, leases. Products and pricing models keep launching, and each one adds another path into the books. Our goal is a continuous close with zero material issues, with audit-grade controls. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong backend engineering in a statically typed language such as Go, Java or Rust, or Python at scale
- Experience designing and running high-volume, event-driven systems (publish/subscribe, streaming) with idempotent processing, replay and backfill
- Experience designing relational data models and writing SQL for complex, versioned data
- Experience designing APIs, SDKs and service-to-service integrations used by other teams
- Experience building systems with auditability, access control or compliance requirements
- Experience turning requirements from non-engineering partners into technical designs
More about this role
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.
Every dollar Anthropic earns and every dollar it spends on compute has to reach the books correctly, and the volume grows with Claude usage. That data comes from across the company and from partners: usage and billing, compute, infrastructure assets, leases. Products and pricing models keep launching, and each one adds another path into the books. Our goal is a continuous close with zero material issues, with audit-grade controls.
You’ll build the platform that gets us there: one front door for every finance event, an append-only ledger, and an accounting engine that posts each event exactly once. This is a distributed systems problem with an unusually high correctness bar. The system has to be right to the cent, replay cleanly and explain any number as of any date, at that volume. You will shape its architecture. Its users include Claude agents, which read and...
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