Product Engineer

## About the role Kyra is an enterprise influencer marketing platform working with brands like SharkNinja, Coach, H&M, Ray-Ban, and Converse. Our platform manages the full lifecycle — creator discovery, campaign management, content review, payments, analytics, and AI-powered intelligence across TikTok, Instagram, and YouTube. Product Engineer At Kyra, we build software differently. Our product team uses AI-native tooling (Claude Code, Cursor, agents) to go from requirement to working product in days, not months. This isn't about prompting your way to a prototype. It's about owning the full lifecycle: sitting inside a client's workflow, finding what's broken, and shipping the fix yourself. The Product Engineer sits at the intersection of three disciplines that used to require three different people. You think like a PM, ship like an engineer, and use AI as leverage to collapse the gap between them. The heads of product are anchors and the product builders/ engineers will report into them, there are 'zones' which are ever changing areas of the product. Builders can be assigned multiple zones. ## What you'll ship First 30 days. Get deep on one enterprise client. Shadow their campaign workflow end to end. Ship one working internal tool that removes a concrete piece of manual work - a creator-brief matcher, a contract-review assistant, a content-approval router. In production, used by a real team, by week four. First 90 days. Own the last-mile build-out for two enterprise accounts: - LLM pipelines for creator matching, brief analysis, or performance summarisation with real evals, not vibes - Custom integrations into the client's existing stack (CRM, DAM, finance systems) - Workflow automation that replaces 3–5 tools with one surface inside Kyra - Shipping to production weekly, measured on what the client stopped doing manually You're the person Nicholas pulls into the room when a new enterprise client signs. You've built reusable primitives (eval harnesses, integration scaffolds, prompt libraries) that the next Product Engineer inherits. You have a point of view on where the platform needs to go, and you're trusted to make the call. ## The work, specifically - Join customer calls. Extract the real requirement underneath the stated one. Go build it. - Design and ship LLM features to production - including the evals that keep them honest - Build custom integrations and workflows the platform doesn't do out of the box - Make product decisions under uncertainty: scope sensibly, trade speed against correctness, know when something is good enough to ship and when it isn't - Think adversarially about your own work. Cover the edge cases. Catch the bug before the user does. ## Stack - Backend: TypeScript / Node, Python for ML and LLM workflows - Frontend: React, Next.js - LLMs: Anthropic API (Claude) as primary, in-house eval tooling - Infra: Vercel, AWS, Postgres - AI tooling: Claude Code, Cursor, agents - expected fluency, not novelty You don't need every line of this on day one. You do