$ cat jobs/staff-software-engineer-l6-developer-productivity-platform-s-netflix-e87afd6272a4.json
Staff Software Engineer (L6) - Developer Productivity — Platform Systems, AIMS E
At Netflix , our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. AI for Member Systems (AIMS) runs the AI systems behind every recommendation, search result, and personalized experience for 300M+ members. Hundreds of researchers and engineers across AIMS depend on a shared inner loop — build and test infrastructure, CI/CD, local and remote dev environments, and the tooling that takes an idea from a notebook to a production experiment — to get their work done every day. As the AIMS AI/ML stack scales and modernizes, that inner loop has to scale with it, or it becomes the bottleneck on everything else. Platform Systems is the engineering foundation of AIMS, owning reliability, scalability, cost efficiency, and developer experience across the org. We're looking for a Senior/Staff AI Software Engineer to own developer productivity for AIMS: the build, test, and iteration loop that our ML researchers and engineers rely on daily. This is a cross-cutting, high-leverage role — improvements here compound across every team in the org, not just one. Responsibilities Own the end-to-end developer experience for AIMS ML practitioners: local and remote dev environments, build and test infrastructure, CI/CD pipelines, and the tools researchers use to move from idea to production experiment. Identify friction in day-to-day engineering and research workflows firsthand, and design tooling and abstractions that remove it at the root rather than patching around it. Design, build, and operate large-scale build and CI/CD systems that keep build, test, and iteration times fast as the codebase, model count, and headcount grow. Partner directly with ML researchers and engineers embedde
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