You'll design schemas, build APIs, ship UI, and operate production systems on GCP, and you'll be judged on the quality of what you ship. On top of that foundation you'll own the product side of your work too: you'll take a product question ("why did this model stop converting?"), A recent example: our market-prices system, where one engineer owns everything from the scraper data contract to the calibration report that tells us when a rival outbids us. The role sits deliberately between product, design, and engineering. There's no separate PM writing your tickets. Our design system lives in code, so you'll make the design calls and ship polished UI without waiting for a handoff. And you'll build for two kinds of users: people and AI agents. A note on AI-native: AI multiplies engineering skill, it doesn't replace it. Vibe coding doesn't survive contact with a production pricing system. js UI, tests (Jest, Playwright), deployment, and the metric afterwards. Build the systems behind pricing: market-data ingestion with dry-run validation, append-only observation datasets, and calibration reports ranked by competitor gap. Ship customer-facing product across the quote, checkout, and merchant-embedded flows. Extend our agent surface:



write skills and plugins for the team, build MCP servers for internal tools, and improve the public Claude connector. Automate operations with agents: alert triage, changelogs, reports, data checks. If you do something twice, you teach an agent to do it. Have 5+ years of qualified experience building and running production systems with TypeScript, Node.js, React/Next.js, and PostgreSQL. Have built and shipped production APIs and integrations, ideally on serverless GCP (AWS also valuable). Care about data modeling: you understand why append-only tables, point-in-time correctness, and partitioning matter for data that will train models later. Work AI-natively, and this is non-negotiable: agentic coding tools (Claude Code, Codex, or similar) are your daily driver, you write your own skills, plugins, and MCP servers, and you can defend every line an agent writes. Have design sensibility: you sweat UI details and can ship a polished interface from a design system without a handoff. Machine learning experience. The market-price dataset is built to be trained on, and you'd partner with the ML seat that owns pricing calibration.

📌 Senior Product Engineer, Applications (Canada)
🏢 SELLIT9
📍 Canada