Research Engineer, AI (University Grad)
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 â€" we're just getting started.
In this role, you will embed deeply with researchers at Facebook AI Research (FAIR) and develop experiments and prototypes at the frontier of AI Research. You will need to engage with research topics and cover new domains quickly; build deep expertise with Facebook data and tools; apply high standards to the research code around you and develop an ability to identify highly impactful projects in a complex and unexplored domain. You will gain valuable experience in artificial intelligence and AI, publish academic papers and help push forward the understanding of learning and intelligent systems.
- Perform research to advance the science and technology of intelligent machines.
- Perform research that enables learning the semantics of data (images, video, text, audio, and other modalities).
- Devise better data-driven models of human behavior.
- Work towards long-term ambitious research goals, while identifying intermediate milestones.
- Influence progress of relevant research communities by producing publications.
- Contribute research that can be applied to Facebook product development.
- Experience in developing and debugging in C/C++ and/or Python.
- Ability to obtain and maintain work authorization in the country of employment in 2019.
- M.S. degree in Computer Science or related quantitative field.
- Experience building systems based on machine learning (especially deep learning) methods.
- Experience using frameworks like PyTorch, Caffe2, Tensorflow, Theano, Keras, and/or Chainer.
- Experience with storage systems, distributed systems, HPC, compilers, and/or CUDA programming.
- Research and software engineer experience demonstrated via an internship, contributions to open source, work experience, or coding competitions.
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