Research Scientist, Infrastructure
(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.
Facebook is seeking candidates to join our Infrastructure team with expertise in numerical optimization and high-performance computing for ML & AI applications. The role involves working closely with several ranking teams like Ads, Feed, Search, Instagram and a number of other teams at Facebook to optimize and determine bottlenecks in current state of the art Deep Learning algorithms. This position is full-time and located in our Menlo Park office.
- Applying coding skills to platform development projects in partnership with other engineers on ranking and infrastructure teams
- Adapting machine learning and neural network algorithms and architectures to exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
- Understand computer architecture to convert algorithmic bottlenecks into specific hardware optimizations
- BS degree in Computer Science, Electrical Engineering or other technical field.
- Experience with SIMD, cache and memory access patterns on x86_64 platforms, ARM, and CUDA.
- Experience with multi-thread and or multi-process programming, such as pthread, OpenMP, and MPI.
- Experience in performance benchmarks with heterogeneous systems including CPU, GPU and ARM.
- Experience with distributed communication protocols such as RDMA, GPUDirect, and software frameworks such as ZeroMQ and MPI.
- Experience building systems based on machine learning and/or deep learning methods
- PhD degree in Computer Science, Electrical Engineering or other technical field
- Knowledge in deep neural networks, including CNN and RNN
- Knowledge in fixed point math and optimizations
- Knowledge in designing scale out infrastructure systems
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