Technical Program Manager - Infrastructure Engineering

Netflix is a company dedicated to entertaining the world through innovative storytelling and technology. They are seeking a Technical Program Manager to lead critical initiatives across their Cloud Infrastructure and AI Platform, focusing on scalability, modernization, and internal partnerships.

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 milestones, ownership, and success criteria
• Drive execution rigor by tracking progress, managing dependencies, and proactively identifying risks, trade-offs, and mitigation strategies while implementing minimal process
• Communicate clearly and consistently with technical leadership and stakeholders on progress, risks, and decisions
• Use insights from program execution to influence platform evolution, architectural direction, and operational practices over time

Requirements:

• 7+ years of experience leading large-scale technical programs in infrastructure, internal platforms, or distributed systems, working directly with engineering teams
• Experience driving programs across infrastructure layers to develop scalable platforms that support diverse business needs
• Proven experience creating partnerships with cross-functional teams, driving large-scale technical strategies, debating technical approaches, and building long-term scalable solutions with engineers
• Proven ability to identify gaps in solutions and weigh in on product vs. technology trade-offs
• Excellent written and verbal communication skills, with the ability to articulate technical constraints, trade-offs, and business impact to diverse audiences
• Self-starter who enjoys quickly bringing organization and direction
• Proven ability to operate in 0→1 and evolving problem spaces, quickly bringing order and execution discipline without adding too much process
• Understanding of challenges in high-scale distributed systems, architectures, and data layers
• Experience with fleet-wide cloud efficiency and/or building a culture of cost efficiency
• Experience with machine learning training and experimentation
• Experience with machine learning pipelines
• Experience with inference at production scale
• Experience applying AWS cloud services at a large scale
• Experience with Kubernetes used natively in platform services
• Experience with 3rd party vendors/tools for training and inference experimentation uses