Minimum qualifications:- Bachelor's degree in Computer Science or equivalent practical experience.
- 15 years of experience in software engineering.
- 10 years of experience managing and leading large-scale distributed engineering teams.
- Experience managing international teams and driving cross-site organizational alignment.
- Experience leading infrastructure engineering organizations, specifically managing control planes, cluster management systems, or distributed job scheduling platforms.
Preferred qualifications:- Experience leading enterprise-level AI transformations, including scaling TPU/GPU accelerator infrastructure, and accelerating the transition of complex ML research innovations into high-performance, production-ready developer platforms.
- Ability to navigate complex matrixed organizations, influence technical strategy at the industry level, and drive convergence across legacy and modernized stacks.
- Domain expertise in distributed resource management, machine learning training infrastructure, hardware accelerator orchestration (GPUs/TPUs), and large-scale cloud computing platforms.
- Technical expertise in designing, building, and operating global-scale scheduling and orchestration systems, specifically specializing in multi-cell/multi-tenant scheduling ecosystems, throughput-oriented batch workloads, and resource optimization.
About the jobA core suite of systems and infrastructure manages Google's global orchestration for throughput-oriented workloads across various fleet locations, maximizing resource efficiency on a massive scale. Specializing in accelerator scheduling and massive-scale Machine Learning (ML) training, this infrastructure serves both internal Google fleets and the Google Cloud Platform (GCP). Its capabilities are continuously expanding to encompass the entire traditional compute fleet alongside the accelerator fleet. By offering workload flexibility across spatial, platform, and quota dimensions, these systems achieve exceptionally high fleet occupancy while maintaining robust usability and reliability for third-party customers and all major Google product areas.
As the Director of Engineering for the Global ML Scheduling Infrastructure team, you will lead the strategic direction, engineering execution, and operational excellence of Google's global orchestration layer. Leading a distributed organization of approximately 90 engineers across the US and Poland, you will oversee the mission-critical multi-cell scheduling ecosystem that powers Google's large-scale ML training, inference, and general throughput-oriented batch workloads.
Collaborating with platform, storage, data center, networking, and resource management teams, you will drive new capabilities and support the growth and efficient usage of Google's fleet. You will partner with leads from Google product areas, such as Deepmind, Search, Ads, and YouTube, to accelerate the transition of research innovations to production, with focus on developer experience and acceleration of experimentation and productionization time. You will also contribute to delivering GPUs and Google's advanced internal technology, TPUs, to external customers via Google's Cloud Compute Platform. You will advocate for architectural innovation, drive efficiency initiatives that directly impact Google's infrastructure footprint, and foster a high-performance culture across international sites.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $307000 - $428000 (USD) 30% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities- Define and execute the long-term technical vision and engineering strategy for the Global ML Scheduling Infrastructure functional area, ensuring highly scalable and efficient workload scheduling across Google's global fleet.
- Manage and grow a high-performing engineering organization distributed across the United States and Poland, and collaborating with functions including SRE, PMO, and analytics teams.
- Partner strategically with executive leadership and cross-functional stakeholders across technical infrastructure and product areas to align platform capabilities with Alphabet's accelerating demands for machine learning and throughput-oriented computing.
- Drive architectural evolution and operational excellence across the suite of scheduling microservices, maintaining rigorous SLOs for queuing, fair sharing, cell-level actuation, and multi-tenant resource optimization.
- Advocate for a collaborative and psychologically safe environment that prioritizes talent development, imports and exports top engineering talent, and exemplifies Google's core leadership principles.
