About the role
Profound is the marketing platform for the AI era. As people increasingly turn to ChatGPT, Perplexity, and Gemini to decide what to buy, we give brands the intelligence to see how AI represents them and the Agents to act on it. Today, ~13% of the Fortune 500, plus companies like Ramp, Figma, Chime, Calendly, and DocuSign, use Profound to turn AI Search from a black box into a measurable growth channel. Backed by Lightspeed, Sequoia, Kleiner Perkins, and Khosla Ventures at a $1B valuation, we're a lean, fast-moving team across NYC, SF, Buenos Aires, and London, shipping at a relentless pace and defining a new category at the biggest shift in marketing in 25 years. If you want to do the best work of your career at the frontier of AI, come build it with us. We are hiring a Tech Lead Manager to lead our Answer Engine Insights team in Buenos Aires. Answer Engine Insights is Profound's core analytics product, which gives brands insight into how AI platforms represent them, who's winning in their category, and which sources are shaping those narratives. This is
the team that decides how we collect and analyze AI model responses at scale, how we extract competitive signals, how we model visibility/sentiment/accuracy over time, and how we surface all of it in a fast, reliable product experience. The work is high-leverage, deeply cross-functional, and directly tied to revenue. This is a hybrid role. You'll spend roughly half your time on technical leadership and architecture, and half on people management, hiring, and execution. We expect you to write code, review PRs, and be in the details on critical decisions. We also expect you to grow engineers, run a tight planning process, and own outcomes for
the team. The Answer Engine Insights team owns prompt management and execution (the systems that run prompts across ChatGPT, Gemini, Perplexity, etc. at scale, including configuration, scheduling, and scraping); response analysis (the analytics layer that extracts named entities, sentiment, and accuracy from LLM responses); and data querying (the query layer responsible for efficiently fetching and aggregating data via dashboards, REST API, and MCP).