OpenRouter

Applied AI Engineer

Vaga remota de Engineering com fit claro de localização do candidato.

Publicada15 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeNível aberto

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

Resumo da vaga

Conteúdo da vaga extraído em seções para revisão mais rápida.

Build internal agentic workflows end-to-end on OpenRouter, from prototype to production, that automate support and GTM operations
Integrate our systems with the tools the company runs on, and steadily replace those tools where it makes sense to
Own the reliability of what you ship: evals, guardrails, and monitoring, so automation is trustworthy rather than flashy
Drive measurable impact — deflect support volume, accelerate GTM motions, cut manual toil — and prove it with data
Work directly with support, GTM, and engineering to find the highest-leverage workflows to automate next
Iterate on real usage: ship, measure, improve

Build-over-buy conviction. You build on internal tools first and champion them over polished external options, even when the internal path is slightly more painful. You buy only for genuine expertise, never for something buildable in a day.
You push the last 20%. You've worked on internally-built tooling and made it good enough that people chose it. You're energized by reliability, edge cases, and polish, and you don't delegate the hard part.
Domain fluency in support or GTM. Hands-on with ticketing, CRM, sales ops, or customer-facing workflows, so you know which work is actually worth automating.
Architectural range. You understand systems end-to-end and can jump into any part of one. Deep backend orientation; front-end work is not the point here.
A security mindset. You'll be building integrations with broad internal access. You think about permissions, blast radius, and the second-order effects of what an agent is about to do before it does it.
Evals and data rigor. You measure automation impact quantitatively rather than asserting it.
Excellence, time-boxed. A near-perfect bar, but disciplined enough to ship rather than polish forever.

Experience across three or more agent harnesses. We don't need expertise in any one of them; we want to see that you've tried things.
You've built your own agent harness for a workflow
Production LLM/agentic systems shipped with strong engineering fundamentals
Open-source contributions to dev tooling or agent frameworks

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