About the role
About Exa Exa is building a search engine for the AI era. Our Search API currently powers Agents, Fortune 500s and AI labs as we transform an industry that hasn't been disrupted since the 90s. We're a largely SF-based team of ~100 from Harvard, MIT, Meta, Google Research, ex-founders & dropouts alike. We are rapidly building the most intelligent search engine in history. We're high agency, low-ego, and united by the feeling that this is one of the last problems worth getting right. We raised a $250M Series C led by a16z — with Benchmark, Lightspeed, and YC doubling down — bringing Exa's valuation to $2.2 billion.
The role Revenue Systems & Technology at Exa is about building the operational backbone that powers our entire GTM engine — from first touch to renewal. You'll architect and build Exa's revenue technology stack from the ground up: CRM, marketing ops, sales engagement, customer success, forecasting, conversational intelligence, and the data layer that ties them together. We're hiring someone to build this shoulder-to-shoulder with the GTM team so we can scale faster and operate sharper. You'll own the architecture end-to-end, working closely with Sales, Marketing, CS, Finance, and Engineering to turn a greenfield stack — no legacy debt, no inherited Frankenstein — into the system that carries Exa from early scaling through IPO. This is a builder seat, not a Salesforce-admin role. Expect to operate with autonomy on the systems you own — and to build them with an AI-native mindset, not stitch together point solutions.
What you'll do • Architect and ship Exa's end-to-end revenue tech stack: CRM (Salesforce), sales engagement, customer success, CPQ/billing, forecasting, conversational intelligence — unified by one extensible data layer. • Integrate the marketing ops (MOPS) stack into the revenue layer for pipeline tracking and intent scoring, even as MOPS itself sits with the marketing team. • Own the CRM end-to-end: object model, automation, approval workflows, pipeline configuration, forecasting methodology, data hygiene. • Deploy AI agents across the customer journey: lead routing, deal scoring, next-best-action, real-time pipeline risk detection, renewal and expansion signals. • Build the infrastructure that lets those agents run reliably at scale: structured data pipelines, event-driven triggers, API orchestration, feedback loops. • Own build-vs-buy. Default to open, composable platforms; add a middleware layer so new tools plug in cleanly instead of piling up tech debt. • Keep a canonical data model across every GTM system so analytics, reporting, and agents all run off one source of truth. • Stand up forecasting and pipeline analytics: stage velocity, conversion, segment health, and the leading indicators that feed weekly business reviews and board reporting. • Build the dashboards that give Revenue Leadership real-time visibility into pipeline, forecast trajectory, and customer health. • Collaborate in building out the Revenue Systems team over time.
