Meta's global network comprised of cutting-edge platforms, is looking for a Network Modeling and Optimization Engineer to join the backbone and edge engineering team. This team is responsible for designing, implementing and supporting one of the world’s largest and complex networks. As a Network Modeling and Optimization Engineer, you will have a unique opportunity to influence optical and IP architectures, strategic network acquisitions, network design, deployment, and operations to shape the future network to accommodate hyper-exponential growth as well as internal product requirements.

Responsibilities

Work with various teams to understand Meta's network, user base, performance constraints, and growth requirements
Create modeling framework for various networking problems such as cross-layer optimization under constraints such as latency/availability, demand uncertainty, risk assessment, and data center optimization
Data analysis from a large number of data sources to create a network strategy for capacities, location and facilities
Work with procurement and other teams to devise strategies on hardware and network acquisitions around the globe
Own the design, development, testing, and tuning of future capacity and topology models

Minimum Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Experience using concepts of operations research, stochastic optimization, machine learning, queuing theory, probability theory to construct models for solving network optimization problems
Experience creating formulation using commercial mathematical optimization software like: Xpress, Gurobi, CPLEX, and other similar optimization tools
2+ years of experience coding in higher-level languages (e.g., Python, C++, Go, etc.) coupled with experience creating models for optimization

Preferred Qualifications

Experience with large data sets and distributed computing (Hive/Hadoop)
Graduate work experience (masters or PhD) in the area of operations research, stochastic optimization, machine learning, queuing theory, probability theory

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