Product Finance & Strategy, Monetization

Join a team focused on high-impact AI and help shape how Anthropic monetizes models for enterprise and priority industries. This hybrid role in San Francisco combines strategic finance, pricing, and product economics, with clear ownership from pricing and packaging through to reporting and executive decision-making.

What you’ll do

• Own model monetization end to end, building and maintaining financial models that connect token economics, inference and compute costs, and margin to pricing and packaging decisions across the model family and API
• Act as the Finance & Strategy partner to Enterprise and Verticals product teams, bringing the financial lens to what gets built for enterprises and priority industries
• Develop business cases for enterprise and vertical product investments, including opportunity sizing, model product economics and pricing, and evaluation of return across competing roadmap bets
• Partner cross-functionally with product, go-to-market, and compute stakeholders to assess pricing and packaging changes, new model launches, and consumption-based offerings, and translate analysis into clear, opinionated recommendations
• Analyze unit economics and gross margin drivers, surfacing trends, risks, and opportunities that should guide monetization and product strategy
• Track competitive and market dynamics for AI monetization and enterprise adoption, providing an outside-in view to sharpen strategy
• Form and defend a point of view on monetization and product investment decisions, delivering concise analyses that drive resolution with executives and cross-functional partners
• Coordinate with finance, FP&A, and accounting to keep decisions aligned with broader financial strategy and reporting
• Build and maintain reporting dashboards that track key pricing, margin, consumption, and product performance metrics

Skills and experience

• Advanced modeling: ability to build and maintain a complex operating model from scratch
• Ownership mindset: demonstrated ability to drive a workstream independently from question through decision
• Analytical judgment: comfort forming an opinion, defending it, and updating conclusions as analysis evolves
• AI awareness: follow AI closely and maintain a view on where it is going
• Clear communication: explain complex financial information to non-finance audiences
• Mission alignment: commitment to building safe, transformative AI systems
• SQL

Preferred qualifications

• 7+ years in strategic finance, product finance, pricing, investment banking, private equity, growth equity, or management consulting
• Operating experience inside a company (not solely advisory or investing)
• Direct pricing or monetization ownership, such as setting or revising list prices, designing tiering and packaging, running pricing research, or measuring the impact of pricing changes
• Experience partnering with product on business cases, investment decisions, or roadmap prioritization
• Exposure to consumption-based models including usage-based, API, or developer platform pricing
• Interest in compute economics and cloud infrastructure
• SQL proficiency

Compensation and benefits

• Salary: USD 190,000 - 240,000 per year
• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space for collaboration with colleagues

Location and logistics

• Location: San Francisco, CA (hybrid)
• Hybrid policy: expected to be in an office at least 25% of the time (some roles may require more)
• Education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Visa sponsorship: Anthropic sponsors visas and will make reasonable efforts to support an offer, with an immigration lawyer retained
• Application encouragement: encouraged to apply even if you do not meet every qualification

How Anthropic works

Anthropic believes the highest-impact AI research will be “big science,” with a single cohesive team focused on a few large-scale efforts. The organization values impact toward long-term goals of steerable, trustworthy AI, and treats AI research as an empirical science with frequent research discussions. Communication is highly valued across the organization.