Network Hardware Scientist, PhD Intern (Menlo Park, CA) Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started. Network HW Scientist (Menlo Park, CA) Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started. The Hardware Network Engineering team is looking for a Network Data and Simulation Scientist to develop modeling and simulation platform for Facebook's global network comprised of cutting-edge switches and routers. This team is responsible for the design, implementation and support of one of the world's largest networks. As a Network Modeling Scientist, you will have a unique opportunity to shape the future networks of Facebook by providing the technical requirements and steering the industry and ecosystem. Responsibilities * Data driven prediction and optimization of network capacity based on FB specific services and their organic and disruptive growth dimensions * Identification and recommendation of HW component requirements and alignment of HW technologies to FB growth * Identification and data driven contextualization and quantification of disruptive trends in Infra * Development of tools, algorithms, and modeling platforms to enable service allocation, growth, and network scenario planning Qualifications and responsibilities: * Hands on and intellectual capacity to not only mine and obtain data, but also make sense of it, make it consumable by humans and algorithmic models, and apply growth dimensions to it in order to extrapolate and extend to future * Hands on ability to define and develop predictive models and the scope for them * Validation of models against actual and observed data and calibration of model * Objective and data driven mind, rooted in fundamentals * Strength in mathematics, modeling, simulation, and data derivation, analysis, and consumption * Capable to synthesize the data to a conclusion * Ability to understand and abstract FB services, their network traffic profile, demand, and growth dimensions * Strength in network not strictly required but familiarity is preferred * Ability - based on the data - to initiate projects, land projects and change the necessary eco-system * The ability of the candidate is to change the entire industries Day to day job: * Learning: Understand FB services, how they work, how they grow, and what demand they put on network * Abstract FB services as the sources of traffic * Mine network data from fbflow and dcflow * Construct mathematical models to produce futuristic data sets based on current network and disruptive trends * Develop end-to-end models and simulate with various stimulus models * Developing algorithms, writing and tweaking code for higher performance, getting around compute and memory bottlenecks, experimenting with distributed processing * Validate and calibrate models against actual observed data