Engineering Manager, 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.

We're looking for an Engineering Manager to lead the engineers who 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. You’ll hire and grow the team, and hold the bar for how the platform is built and run in production. Its users include Claude agents, which read and write through the same APIs and controls as people.

You'll partner with engineering teams in Billing, Compute and Infra on shared contracts and APIs, and with product owners and finance operations, who set the priorities and define the needs. You don't need a finance background. If you have led teams that build backend systems where correctness matters, we will teach you the domain.

Minimum qualifications

• Lead and grow a team of engineers: hire, coach and hold a high bar for engineering quality
• Own delivery of the front door for finance events and its governed contracts, and the accounting ledger and engine, so every event is posted exactly once and can be traced back to its source
• Be accountable for the production services that other engineering teams call in real time, such as the tax and collections services in the billing flow
• Make sure finance apps and agents have the platform support they need: SDKs and frameworks, scoped access, SOX guardrails, observability, and the capabilities finance needs that claude products do not provide out of the box
• Define and own the reliability and correctness bar: SLOs on the services other teams call, tests that check key upstream calculations on every change, and daily accuracy and completeness checks from source to books
• Align on technical design, data contracts and APIs with engineering leaders in Billing, Compute and Infra
• Drive down the time it takes finance to support a new product, program or compute buildout
• Have led teams that build and run high-volume backend systems where correctness matters, such as ledgers, billing, payments, metering or other systems of record
• Have built or run event-driven systems with idempotent processing, replay and backfill, and know what it takes to keep them correct in production
• Have sound judgment on platform boundaries, data contracts and API design
• Have hired and grown engineers, and set engineering standards for platforms
• Work well across functions, with upstream engineering teams, finance and auditors, and explain technical trade-offs clearly to non-engineering partners, in writing and in the room

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.

• Have 3+ years of engineering management experience, with 8+ years total in the industry
• Built developer platforms or paved paths used by other builders
• Worked with Go and with streaming systems such as Pub/Sub or Kafka
• Built 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.