Program Manager III, Software Engineering, YouTube

A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Program Manager at Google, you’ll lead complex, multi-disciplinary projects from start to finish — working with stakeholders to plan requirements, manage project schedules, identify risks, and communicate clearly with cross-functional partners across the company. Your projects will often span offices, time zones, and hemispheres. It's your job to coordinate the players and keep them up to date on progress and deadlines. This Program Manager will drive YouTube’s multi-year ML Infrastructure strategy across a comprehensive, balanced portfolio covering the complete machine learning lifecycle: training modernization, next-generation serving, and frontier model scaling. This role offers high-leverage technical leadership at unmatched scale: you will address the core compute and architectural bottlenecks of frontier AI models, directly shaping how hundreds of engineers and researchers build the recommendation systems experienced by over two billion people daily. This role offers an end-to-end vantage point across YouTube's AI infrastructure stack. You will ensure that breakthrough modeling techniques, distributed training frameworks, and low-latency production serving platforms evolve in lockstep to support YouTube's next generation of recommendation systems. In this role, you will be the connector between YouTube’s recommendation research scientists, systems software engineers, Site Reliability Engineers (SRE), hardware operations, and Google-wide platform teams. You will lead high-stakes infrastructure transitions, establish operational excellence for compute allocation and duty cycles, and partner closely with engineering leadership to steer the future of YouTube's AI infrastructure. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $159000 - $230000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Lead cross-team migrations to next-generation ML frameworks, runtimes, and training systems across distributed models. Drive the strategic roadmap and operational delivery for production ML serving platforms, ensuring low-latency inference, high availability, and zero-downtime rollouts. Partner with capacity planning and engineering teams to optimize compute scalability, hardware efficiency, and accelerator (TPU/GPU) utilization. Manage cross-functional dependencies, milestones, and technical blockers across modeling, systems, SRE, and platform teams. Establish operational cadences, proactively manage technical risks, and communicate program status, roadmaps, and trade-offs to executive leadership. Minimum qualifications: Bachelor's degree or equivalent practical experience. 5 years of experience in program or project management. Experience with the software development life cycle and managing engineering programs. Experience working with engineering teams. Preferred qualifications: 5 years of experience managing cross-functional or cross-team projects. Proven experience driving large-scale software migrations or platform modernizations across dozens of participating engineering teams without regressing product latency or stability. Technical background (degree in Computer Science, Engineering, Mathematics, or experience with software engineering, scripting, distributed systems architecture, or deep learning systems). Working AI fluency and familiarity with machine learning concepts (e.g., model training, inference serving, evaluation, and compute accelerators) with the ability to converse credibly with ML engineers and researchers. Familiarity with compute resource planning, capacity budgeting, hardware efficiency, or accelerator duty-cycle analysis (TPUs, GPUs).