Staff Software Engineer, Continuous Fleet Transformation and Optimization
Minimum qualifications:
- Bachelor's degree in Computer Science, a technical field, or equivalent practical experience.
- 8 years of experience in designing and developing large-scale distributed systems and software.
- Experience with large-scale distributed systems, software architecture, computational issues and advanced algorithms.
Preferred qualifications:
- Experience with mixed integer programming/linear programming or data center capacity planning/management.
- Experience with optimization problems or supply chain systems.
About the job
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The team is optimizing Google's fleet using Operations Decision Support and discrete algorithms, transitioning from Mixed-Integer Programming (MIP) models to Machine Learning (ML)-powered simulations for predictive, self-governing systems.
Responsibilities
- Guide the technical blueprint for Fleet Transformation, ensuring Google's infrastructure manages Artificial Intelligence/Machine Learning (AI/ML) compute needs.
- Design and architect simulation engines and solvers for multi-dimensional constraints (e.g., power, space, network) on global datasets with multiple variables.
- Advance MIP models by migrating fleet optimizations from localized to integrated, cluster-wide solutions for better resource utilization and efficiency.
- Guide the self-driving Fleet initiative by creating automated policy-enforcement layers and lifecycle actions.
- Mentor engineers, promote cross-functional synergy with Planning and Data Center Operations, and ensure reliability of global systems.