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.
Anthropic is seeking a Financial Data and Controls Lead to join our Billing Platform team. Billing Platform builds the systems customers use to transact with Anthropic: subscriptions, usage billing, quote to cash, invoicing, and payments. You'll be the dedicated owner of billing accuracy, reconciliation, and financial controls across those systems, making sure the right checks run, the right controls are in place, and the audit evidence exists.
Our billing systems and pricing change often, and each flow needs to meet SOX requirements. Accurate billing is what lets us launch products and pricing quickly and still close the books on time. The hard problems today include validating negotiated pricing from contract to invoice, handling the many forms of credits, app store billing, and keeping controls current while the systems under them are replaced.
This is a foundational role and the first hire of its kind on the team. You'll lead a working group of the reconciliation and controls owners from each billing engineering team, and you'll set the approach they follow. You'll work closely with Finance Systems, Accounting, Internal Audit, and Data Engineering.
The role sits in Engineering and works with Finance every day. Our Data organization builds the pipelines and owns the warehouse, and you'll use that data and partner with that team. We're looking for an engineer or data practitioner with strong accounting domain knowledge, who can talk to engineers about systems and to Finance and auditors about risk and materiality.
Key responsibilities- Own reconciliation and accuracy checks for critical billing data (quantity, price, discounts, and invoices) across our product platform, third-party billing and payment providers, app stores, cloud marketplaces, and in-house systems
- Design and run detective controls that catch billing errors before they reach the accounting books
- Partner with billing engineering teams to turn repeat checks into automated verifiers
- Review billing changes that have financial impact, flag the ones that touch SOX or IT application controls (ITACs), and make sure functional test evidence is in place
- Make SOX review a standard, lightweight step in how billing changes ship, treated like a security review, so launches are not delayed by it
- Own the end-to-end test strategy and audit evidence for those changes, working with Finance Systems, Data Engineering, Accounting, and Internal Audit
- Triage billing errors, prioritize backfills and fixes with the owning teams, and track materiality over time
- Define and maintain the data contracts between billing and finance systems, so downstream teams can find and trust the data they need
- Lead the working group of reconciliation and controls owners across billing engineering teams, through influence and not reporting lines
- Act as the Billing owner, alongside Accounting and Finance Systems, for translating the product and pricing roadmap into financial impact, test strategy, and process updates
- Experience building or owning a financial ledger, billing reconciliation, or cash reconciliation system end to end, one that Accounting relied on to close the books
- Hands-on SQL skills strong enough to take two messy datasets (for example usage events and invoices), match them, find the breaks, and explain the root cause
- Accounting domain knowledge (order to cash, month-end close, and materiality) sufficient to tell which billing changes have financial impact
- Experience working with SOX and Internal Audit teams on IT application controls, detective controls, transaction monitoring, functional test evidence, and auditor walkthroughs
- Experience defining data contracts between billing or product systems and finance systems such as the ERP, general ledger, or revenue recognition
- Track record of driving work across Engineering, Finance, Accounting, and Audit without direct authority
- Ability to explain technical issues to Finance and auditors in their terms, and financial risk to engineers in theirs
- Interest in AI and a commitment to Anthropic's mission of building safe, beneficial AI systems
- Background as a backend, data, or analytics engineer who built a financial ledger or cash reconciliation system from scratch
- Experience running these systems under public company reporting requirements, and scaling them as the company grew
- Experience with usage-based or consumption billing
- Experience with multi-party billing, such as marketplaces, or app stores
- Experience turning manual checks into automated verifiers with engineering teams
- Experience remediating a control deficiency, or standing up a controls program while the underlying systems were changing
- A track record of using Claude or other AI tools to automate reconciliation, testing, or evidence collection, with checks that hold up to review
- Experience with billing and finance tools such as Stripe, Metronome, Zuora, NetSuite, or Workday Financials
- Python for data analysis or automation
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.
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.
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.
Skills Required
- Experience building or owning a financial ledger, billing reconciliation, or cash reconciliation system end to end for accounting close
- Strong hands-on SQL skills for matching datasets, identifying breaks, and explaining root causes
- Accounting domain knowledge covering order to cash, month-end close, and materiality
- Experience working with SOX and Internal Audit teams on IT application controls, detective controls, transaction monitoring, functional test evidence, and auditor walkthroughs
- Experience defining data contracts between billing or product systems and finance systems such as ERP, general ledger, or revenue recognition systems
- Track record of driving work across Engineering, Finance, Accounting, and Audit without direct authority
- Ability to explain technical issues to Finance and auditors and financial risk to engineers
- Interest in AI and commitment to building safe, beneficial AI systems
- Bachelor's degree or equivalent combination of education, training, and experience
- Relevant field of study demonstrated through coursework, training, or professional experience
- Background as a backend, data, or analytics engineer who built a financial ledger or cash reconciliation system from scratch
- Experience operating financial systems under public company reporting requirements and scaling them as the company grew
- Experience with usage-based or consumption billing
- Experience with multi-party billing, marketplaces, or app stores
- Experience turning manual checks into automated verifiers with engineering teams
- Experience remediating a control deficiency or establishing a controls program while systems were changing
- Experience using Claude or other AI tools to automate reconciliation, testing, or evidence collection with reviewable controls
- Experience with Stripe, Metronome, Zuora, NetSuite, or Workday Financials
- Python experience for data analysis or automation
Anthropic Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anthropic and has not been reviewed or approved by Anthropic.
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Healthcare Strength — Health coverage for employees and dependents is described as comprehensive across medical, dental, and vision, alongside robust mental-health resources. Feedback suggests this breadth, paired with life and income protection, is a standout element of the package.
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Parental & Family Support — Family-building support includes inclusive fertility benefits and an extended paid parental leave policy. Feedback suggests these programs are positioned as company‑wide and accessible rather than one‑off perks.
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Wellbeing & Lifestyle Benefits — Everyday support spans wellness/time‑saver stipends, education and home‑office stipends, commuter benefits, daily meals/snacks, and relocation assistance. Feedback suggests these perks meaningfully supplement core pay and healthcare.
Anthropic Insights
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
