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Arena Intelligence is building the evaluation layer for AI models, helping labs and enterprises understand how frontier systems perform in real-world workflows. Founded by UC Berkeley researchers, the company has built a platform used by tens of millions monthly to evaluate AI model performance across agentic coding, creative generation, professional productivity, and beyond.
You'll join as GTM Strategy & Operations Lead, reporting to the Head of Finance. This is a foundational role where you'll shape the commercial strategy and operational infrastructure for a high-growth AI company selling to enterprise customers and leading AI labs.
Key responsibilities include: running go-to-market operating cadence (pipeline reviews, forecasting, business reviews); building pipeline and usage reporting for GTM and finance teams; serving as operational partner to sales leadership with account-level visibility and analysis; defining pricing and packaging strategy including rate cards, discount guardrails, and new product monetization; developing GTM approaches for new market segments from opportunity sizing through coverage models; running the deal desk for large commercial agreements and structuring non-standard contracts; owning quote-to-cash mechanics from order forms through metering, consumption tracking, and billing accuracy; and partnering with product and engineering to ensure new SKUs launch with proper instrumentation and billing integration.
You'll need 6+ years in GTM strategy, revenue operations, sales strategy, deal desk, or management consulting, with experience at high-growth technology companies selling to enterprises. You should have hands-on experience running go-to-market operating rhythms, pricing and packaging decisions, and consumption/usage-based revenue models. Strong SQL or BI tool skills required. You'll be comfortable with AI tools in daily workflow and able to communicate effectively with sales leadership and executives. The role requires high ownership, sound judgment on process-building priorities, and the ability to move fluidly between strategic recommendations and underlying data models.
