About Vailent

Vailent builds AI infrastructure for the materials industry — chemicals, polymers, elastomers, rubber. The companies in this space run on a mess of CRMs, ERPs, point tools, and flat files. We're replacing all of that with one system that turns every interaction, transaction, and physical asset into usable commercial data.

Materials are the foundation of the physical economy — every product humans build, ship, eat, wear, or drive passes through this industry — yet the sector remains under-instrumented, run on fragmented tools and the tacit knowledge of senior operators. Vailent is building the infrastructure the world's most foundational industry will run on for the next century. We're a subsidiary of Vinmar International, which gives us something most startups never get: live tenants, real ERP data, and real users from day one — and a platform built to serve distributors and producers across the entire industry.

About the Role

A Product Led Builder owns a product area end to end — discovery, decisions, and delivery — and ships the product themselves using AI agents. This is not a PM who writes specs and hands them off. It's a PM who builds.

- The spec is the prototype. The prototype becomes the PR.

When AI can generate working code from a precise brief, the slowest step in product development is the handoff between the person who understands the problem and the person who writes the code. This role removes that handoff. You'll sit with sales reps, pricing managers, and product directors across our tenants, figure out what actually slows them down, and put working software in front of them — fast. Engineering reviews and hardens; you own the outcome through to production. Our users are busy people doing demanding work. A rep closing 5–15 deals a week needs tools that keep pace with them. A pricing manager who spends a third of their week tracking feedstock and market news benefits most from clear, timely answers. You'll build for them directly, on surfaces like Catalog, PriceBook, order approvals, and the opportunity tracker.

We ship every week.

Builders at Vailent put something into production every week. Not a demo, not a deck — a change real users can touch: a fix, a slice of a larger feature, or an instrumented experiment. Our users move at the pace of 5–15 deals a week, and the product should keep up with them.

• Slice big bets thin. Larger features get broken into increments that each reach production on their own, behind a flag if needed.
• Every ship is demonstrated. A working screen, a read-back record, or a live payload in the real environment — then a handoff note so the next person starts informed.
• Missed weeks get named. If a week ends without a ship, you say so plainly, say why, and say what unblocks the next one.

What You'll Do

• Own a product area's outcomes. Discovery, prioritization, and delivery for a surface like PriceBook or Catalog. Three months in, we should be able to point to things that exist because you built them.
• Ship to production every week. Keep a steady weekly cadence of production changes in your area, each one demonstrated with users or in the live environment — momentum is part of the job, not a bonus.
• Go to the user first. Run discovery directly with sales reps, customer service reps, pricing managers, and product directors across our current and future customers. Map the real workflow — the spreadsheet, the SAP screen, the email chain — before proposing anything. Then build capabilities that work for every customer, not one-off features for a single tenant.
• Prototype before you debate. Turn a problem into a working prototype in days, not a PRD in weeks. Use it to align stakeholders and test with real users.
• Ship production code with AI agents. Use Claude Code and agentic tooling to generate, test, and open production-ready PRs against the actual repo. Engineering reviews and hardens; you carry it to done.
• Write specs precise enough for an agent. When you do hand off, your brief is tight enough that an AI agent or an engineer can act on it without follow-up questions.
• Respect the data model. Read our schemas and ERP-mapped master data well enough to judge whether AI-generated scaffolding is correct — customer IDs, sales areas, material groups, tenant rules.
• Close the field-to-product loop. Turn support tickets, CSAT signals, and integration issues surfaced by Customer Success into prioritized, shipped fixes.
• Measure what you ship. Define success before you build, instrument it, and report back plainly on what worked, what didn't, and what you cut.

Requirements

• Product judgment first. You've owned product outcomes — not just a backlog. You can explain why you built something, what you chose not to build, and how you knew it worked.

• You've already crossed the line into building. You've used Claude Code, Cursor, or similar tools to ship real software that real people use. Not a weekend demo — something in production. Show us.
• A real technical floor. You don't need a CS degree, but you can read a data model, follow an API contract, run the app locally, and tell when AI-generated code is wrong.
• The evidence reflex. "Done" means demonstrated. You prove things with a working screen, a read-back record, or a live payload — never a green badge.
• Comfort with messy, enterprise data. You've worked with systems that weren't designed to talk to each other — ERPs, CRMs, spreadsheets, flat files — and you know that's where the product problems live.
• Customer credibility. You can sit across from a commercial director at a chemicals distributor, earn their trust, and speak their language plainly.
• High agency, low ego. You push through blockers instead of escalating them, take blunt feedback well, and reverse your own conclusions out loud when the evidence turns.

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