Staff Software Engineer, Infrastructure Location: Seattle, WA - Hybrid Base Salary Range: $210K - $250K (depending upon expertise) + equity Gable helps engineering teams put guardrails around data before problems reach production. We bring data contracts, code-level lineage, and CI/CD enforcement into the development lifecycle so teams can move faster with more confidence. We're an early-stage company building in a complex space, and this role is intentionally broad. We need a staff-level engineer who can own DevOps and infrastructure while still contributing as a strong software engineer. Someone who is autonomously jumping between backend systems, deployment workflows, developer tooling, and production issues - and is energized by that range rather than boxed in by it. The Role This is a high-ownership role for an experienced software engineer with real infrastructure depth. You might have started in backend, full-stack, or platform engineering and gradually taken on DevOps or infrastructure responsibility, but you still like writing code and solving product-adjacent technical problems. You'll work across CI/CD, observability, automation, databases, Docker, cloud infrastructure, and internal tooling. You do not need to be the world's best expert in every domain. You do need strong judgment, a bias for action and making decisions, and the ability to go deep when something breaks or needs to scale. Responsibilities Own deployment, infrastructure, and DevOps systems end-to-end Build and improve CI/CD pipelines that enable fast, reliable releases Write tooling and automation for infrastructure, operations, and integrations Contribute to backend systems and software engineering projects where infrastructure and application code intersect Improve local development environments, internal tooling, and developer workflows Build monitoring, alerting, and observability that helps engineers diagnose real issues quickly Strengthen reliability, security, and operational practices across the stack Partner closely with engineers across the company to design pragmatic systems that scale Debug hard problems across code, infrastructure, and runtime behavior Use AI where it meaningfully improves engineering speed, tooling, or operational efficiency