Product Finance & Strategy, Monetization

Posted 3 Days Ago
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San Francisco, CA, USA
In-Office
240K-325K Annually
Senior level
Artificial Intelligence • Natural Language Processing • Generative AI
The Role
Lead model monetization and enterprise/verticals financial strategy: build and maintain token- and compute-linked financial models, size opportunities, evaluate product economics and pricing, partner cross-functionally to recommend pricing/packaging and investment decisions, track margins and market landscape, and create reporting dashboards to inform executives and product teams.
Summary Generated by Built In
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

We are seeking a Finance & Strategy team member to drive two of the most consequential financial workstreams at Anthropic: Model Monetization and Enterprise & Verticals. On one side, you'll own the economics behind how we price and package — spanning list pricing, tiering, and consumption mechanics across our model families. On the other, you'll serve as the Finance & Strategy partner to our Enterprise and Verticals product teams — shaping the business cases, product economics, and investment decisions behind what we build for enterprises and priority industries. Together, these determine how the business world accesses frontier AI..

This is a high-ownership, high-autonomy role for someone who genuinely loves to model and is energized by hard, ambiguous questions. You'll connect token economics, inference and compute costs, and margin to pricing and packaging decisions, and bring that same rigor to enterprise and vertical product investments. We're looking for someone opinionated and decisive, you'll be expected to form a view, defend it with rigorous analysis, and drive it to a decision. A deep, current obsession with AI and a perspective on where the field is heading will make you far more effective here.

If you are passionate about applying financial rigor to the economics of frontier AI, join us in making AI safe and impactful.

Key responsibilities
  • Own model monetization end to end: build and maintain the financial models that connect token economics, inference and compute costs, and margin to pricing and packaging decisions across our model family and API

  • Serve as the Finance & Strategy partner to our Enterprise and Verticals product teams, owning the financial lens on what we build for enterprises and priority industries

  • Build the business cases behind enterprise and vertical product investments: size opportunities, model product economics and pricing, and evaluate the return on competing roadmap bets

  • Partner cross-functionally with product, go-to-market, and compute stakeholders to evaluate pricing and packaging changes, new model launches, and consumption-based offerings, translating analysis into clear, opinionated recommendations

  • Analyze the drivers of unit economics and gross margin, surfacing the trends, risks, and opportunities that should inform monetization and product strategy

  • Track the competitive and market landscape for AI monetization and enterprise adoption, bringing an outside-in view that sharpens our own strategy

  • Form and defend a point of view on monetization and product investment decisions, delivering clear, concise analyses that drive to resolution with executives and cross-functional partners

  • Collaborate with finance, FP&A, and accounting counterparts to ensure decisions are consistent with broader financial strategy and reporting

  • Establish and maintain reporting dashboards that track key pricing, margin, consumption, and product performance metrics

Minimum qualifications
  • A background in investment banking, private equity, management consulting, or a comparable analytically rigorous role

  • Advanced proficiency in spreadsheet-based financial modeling, with the ability to build and maintain complex operating models from the ground up — and genuine enjoyment of the craft

  • Demonstrated ability to take full ownership of a workstream and drive it independently from question to decision

  • The judgment and conviction to form an opinion and defend it, paired with the openness to update it as the analysis evolves

  • A genuine, current obsession with AI — you follow the field closely and have a perspective on where it is going

  • Ability to communicate complex financial information clearly to non-finance audiences

  • Passion for Anthropic's mission to build safe, transformative AI systems

Preferred qualifications
  • Experience across investment banking, private equity, growth equity, venture capital, management consulting, strategic finance, or product finance

  • Operating experience inside a company, not solely in an advisory or investing seat

  • Direct experience owning pricing, packaging, or monetization strategy — for example, setting or revising list prices, designing tiering and packaging, running pricing research, or measuring the impact of pricing changes

  • Experience partnering with a product organization on business cases, investment decisions, or roadmap prioritization

  • Experience with usage-based, API, or developer platform pricing, or other consumption-based business models

  • Knowledge of and interest in cloud computing infrastructure and compute economics

  • Proficiency in SQL

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:
$240,000$325,000 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.

Skills Required

  • Background in investment banking, private equity, management consulting, or a comparable analytically rigorous role
  • Advanced proficiency in spreadsheet-based financial modeling and ability to build complex operating models
  • Demonstrated ability to take full ownership of a workstream and drive it independently from question to decision
  • Judgment and conviction to form and defend opinions, with openness to update as analysis evolves
  • Genuine, current obsession with AI and a perspective on where the field is heading
  • Ability to communicate complex financial information clearly to non-finance audiences
  • Passion for Anthropic's mission to build safe, transformative AI systems
  • Bachelor's degree or equivalent combination of education, training, and/or experience in a relevant field
  • Minimum years of experience correlated with internal job level requirements (unspecified)
  • Experience owning pricing, packaging, or monetization strategy (setting/revising list prices, designing tiering, running pricing research)
  • Operating experience inside a company (not solely advisory or investing)
  • Experience partnering with product on business cases, investment decisions, or roadmap prioritization
  • Experience with usage-based, API, or developer platform pricing or other consumption-based business models
  • Knowledge of and interest in cloud computing infrastructure and compute economics
  • Proficiency in SQL

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.

  • Strong & Reliable Incentives Pay is positioned as top-of-market for many technical roles through a mix of high base pay, equity, and occasional bonuses/signing incentives. Benefits like substantial monthly stipends and employer-paid protections further strengthen perceived total rewards.
  • Healthcare Strength Healthcare is described as comprehensive across medical, dental, and vision, with additional mental-health support. Coverage is framed as robust for employees and dependents, which can materially increase the value of the overall package.
  • Parental & Family Support Paid parental leave is described as notably generous, alongside fertility coverage and other family-oriented supports. These elements broaden the rewards package beyond cash compensation and can improve retention for caregivers.

Anthropic Insights

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The Company
HQ: San Francisco, California
2,500 Employees

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.

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