Posted 1 Sep 2026 (64 days ago) · Job 3020 · Read from the employer's Ashby board, last checked 5 Nov 2026, 16:23 UTC

Strategic Finance Lead - AI

Perplexity · San Francisco, California

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What the posting says

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PayStated
$190,000–$240,000 a year
Employment typeStated
Full time
Work arrangementStated
Hybrid

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ExperienceNot mentioned
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EducationNot mentioned
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Requirements, each with its sentence from the posting

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About the Role

We are seeking a Strategic Finance Lead to help lead the economics of our AI products. In this role, you will be a key partner to our product, inference, and data teams, providing financial expertise to optimize model spend and drive pricing and margin decisions.

In this role, you'll develop deep expertise in the unit economics of AI products, from per-request serving cost to consolidated product-level margin across our portfolio. You'll build the models that inform Perplexity's pricing, packaging, and model-deployment decisions, own the product-margin methodology that reconciles revenue and inference cost from production data, and translate complex model economics into clear financial narratives for leadership.

This is a high-impact role for someone who thrives at the intersection of finance and AI product strategy, and who is energized by building frameworks from scratch in a fast-moving environment.

This position is based in San Francisco and requires in-person attendance 2-3 days per week.

Key Responsibilities

  • Finance lead for inference and model spend, including budgeting, monthly forecasting, variance analysis, and financial plan maintenance

  • Build and maintain product-margin models across the product portfolio, reconciling revenue, serving cost, and gross margin from production usage data

  • Develop deep expertise in model provider contracts, pricing structures, and cost drivers, and surface optimization opportunities across the model portfolio and routing mix

  • Serve as subject matter expert for product unit economics, owning source of truth on cost per request, margin by product and tier, and customer-segment and feature-level economics

  • Own the consolidated model-cost forecast, translating product roadmaps and model transitions into forward-looking spend and margin trajectories

  • Analyze the margin impact of pricing, packaging, and model-deployment decisions, distilled into a framework for product and pricing strategy

  • Partner closely with product, inference, and data engineering to understand how usage and serving cost scale, and translate those technical dynamics into financial frameworks

You May Be a Good Fit If You Have

  • Exceptional analytical skills with an ability to synthesize data into compelling insights and develop complex financial operating models

  • Extraordinary problem-solving and critical thinking abilities to develop new frameworks for assessing product margin and unit economics in a rapidly evolving industry

  • Attention to detail and patience for getting to the source of truth on complex and interconnected usage, revenue, and cost data

  • Comfort working cross-functionally and are adept at communicating complex financial information to non-finance audiences

  • Sound commercial judgment across pricing, packaging, discounting, and the tradeoffs between growth and margin

  • A proven track record of partnering with technical and product teams to drive financial optimization initiatives

  • A bias toward action, strong work ethic, and experience driving operational outcomes under tight timelines

  • Background in AI, ML, or consumption-based business models with complex cost structures

Preferred Qualifications

  • 4+ years of experience in strategic finance, private equity, growth equity, consulting, or investment banking, preferably with software or AI product experience

  • Experience in AI or LLM product financial management, including direct work with model providers or inference platforms

  • Deep expertise in usage-based pricing, subscription economics, and gross-margin and COGS attribution by customer and model family

  • Experience with model serving economics, including how latency, throughput, and token usage flow through to margin performance

  • Proficiency with financial modeling tools

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