Staff Software Engineer, Finance Engineering

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 write through the same APIs and controls as people.

You’ll work closely with SWEs in Billing, Compute and Infra on shared contracts and APIs, and with product owners, finance operations and internal audit to turn accounting and controls requirements into designs. You don’t need a finance background. If you have built backend systems where correctness matters, we will teach you the domain.

Minimum qualifications

• Design and build the front door into finance and its governed contracts: APIs for apps and agents, an event bus for systems that publish, adapters for vendor apps and reports, data-quality gates, and processing that posts every event exactly once, with replay and backfill
• Drive the build of the accounting ledger and engine: an append-only ledger with idempotent posting, a rules framework that turns business events into journal entries, translation into the ERP, and period locks
• Design and build the SDKs and frameworks that finance apps and agents build on
• Build controls into the platform as code: immutable audit trail, maker-checker, scoped access and segregation of duties
• Build and run the production services that other engineering teams call in real time, such as the tax and collections services in the billing flow, and own their reliability: uptime, latency, throughput and correctness targets agreed with those teams
• Shape the platform’s technical direction: architecture, engineering standards and design reviews
• 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
• Strong written and verbal communication, including design docs

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.

• 10+ years of experience
• Experience building developer platforms or paved paths
• Experience with streaming systems such as Pub/Sub or Kafka
• Experience building with LLMs and agents, including MCP

$320,000—$405,000 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.