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Technical Program Manager - Cloud Infrastructure and AI Platform

Netflix - USA - Remote

Posted Mar 29, 2026

Benefits

Parental leave
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401(k) match
Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Schedule

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About this role

Technical Program Manager - Cloud Infrastructure and AI Platform USA - Remote 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. The Role This Technical Program Manager will lead high-impact, cross-functional programs across Netflix's Cloud Infrastructure and AI Platform that deliver meaningful outcomes for both platform reliability and customer needs. You will drive fleet-wide modernization, agility, scalability, reliability, and efficiency, while abstracting complexity for internal customer teams. This includes partnering on migrations, adoption, and scaling of cloud infrastructure platforms and the AI Platform, enabling ML and AI practitioners to seamlessly develop, train, deploy, serve, and manage models. This role requires experience driving cloud infrastructure scalability, reliability, and efficiency at scale, a proven ability to develop deep internal customer relationships, and an understanding of how to adapt cloud infrastructure for changing business needs. Netflix's Cloud Infrastructure serves as the backbone for Netflix's growing scale and engineering productivity, representing a critical strategic capability spanning compute, networking, storage, infrastructure management, performance engineering, and platform services. Responsibilities: Lead initiatives that drive adoption and scalability of infrastructure platforms, including large-scale platform modernization, migrations, and feature rollouts. Partner closely with leaders, engineers, PMs, and practitioners to decompose complex problem spaces into technical execution plans with well-defined

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