The role involves establishing and leading a new team focused on strategic capacity planning and resource optimization for simulation infrastructure. The engineer will design and build production-grade systems to automate capacity planning, demand management, and quota allocation. Responsibilities include implementing sophisticated models for infrastructure demand forecasting and developing algorithms to optimize resource utilization across heterogeneous hardware. The position requires architecting robust data pipelines to ingest telemetry and demand signals. Additionally, the role involves translating resource plans into tangible outcomes and collaborating with cross-functional teams to align technical solutions with business requirements.

Candidates must possess a Bachelor's degree in Computer Science, Mathematics, Statistics, Operations Research, or a related quantitative field, though a Master's or PhD is preferred. Applicants are required to have at least eight years of software engineering experience with a focus on distributed systems or large-scale data processing. Proficiency in C++ or Python is necessary, along with expertise in SQL and large-scale data warehouses. A strong background in quantitative methods such as statistical modeling or optimization is also required.