Facebook Applied Research Scientist and Software Engineers, Speech and Machine Learning in Menlo Park, California
Facebook's mission is to give people the power to share, and make the world more open and connected. Through our growing family of apps and services, we're building a different kind of company that helps billions of people around the world connect and share what matters most to them. 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 make the world more open and accessible. Connecting the world takes every one of us—and we're just getting started.
Facebook is seeking Research Scientists and Software Engineers to join our Speech Team in Menlo Park. We are looking for experienced applied researchers in machine learning and AI with strong software engineering skills. The Speech Team is part of the Applied Machine Learning's organization. The team carries out applied research on audio and speech processing. The team has launched video captioning for Facebook's video products including Ads and Instagram and continues to evolve and optimize its ML / AI algorithms for speech recognition and video understanding offerings for the rest of Facebook. The ideal candidate will have research experience in developing speech recognition systems in different languages. Individuals in this role should be experts in neural networks and machine learning and have experience working on large quantities of data. Experience in neural network based acoustic and language modeling with closed or open source toolkits such as Kaldi, Torch, Tensorflow or CNTK is a plus. The candidate will help Facebook conduct research that support naturally spoken input in more than 70 languages.
Develop highly scalable algorithms based on state-of-the-art machine learning and neural network methodologies
Combine broad and deep knowledge of relevant research domains with the ability to synthesize a wide range of requirements to make significant contributions to the feature roadmap for the applied machine learning platform
Apply expert coding skills to platform development projects in partnership with other engineers
Adapt machine learning and neural network algorithms for training competitive, state-of-the-art models while make the best use of modern parallel environments (e.g. distributed clusters, GPU)
MS degree in Computer Science or related quantitative field with 5+ years of relevant experience, or Ph.D degree in Computer Science or related quantitative field
Knowledge of neural network based modeling
Experience building systems based on machine learning and/or deep learning methods
Knowledge developing and debugging skills in e.g. C/C++, Java, Python, or Lua
Experience with filesystems, server architectures, and distributed systems
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