- Menlo Park, California, United States
- On-site
- Full-time · Senior
- 7+ years
The role involves building and extending large-scale simulations of global traffic placement, solving capacity planning as constrained optimization, building production forecasting for peak demand, and leading technical direction across multiple teams.
Responsibilities
- Build and extend large-scale simulation of global traffic placement: how traffic shifts across edge sites as demand grows and as sites, peering links and backbone paths fail.
- Work with the load-balancing team on simulation fidelity and performance, and validate results against production behavior and live failure tests
- Formulate and solve capacity planning as constrained optimization: where to add compute, peering and caches, given space, power, supply, latency and failure constraints.
- Build and tune the solvers
- Plan capacity for the browsers, proxies and caches Meta's services and AI agents use to reach the internet, and find which one becomes the bottleneck as Meta's internet access needs grow.
Requirements
- Bachelor's degree in Computer Science
- Bachelor's degree in Computer Engineering
- Bachelor's degree in relevant technical field
- PhD in operations research
- PhD in computer science