Research Intern, Machine Learning- Audio (PhD University Student)
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.
Facebook Reality Labs in Redmond, WA is looking for exceptional interns to help us make audio in augmented reality and virtual reality realistic. The goal of this project is to design, develop and deploy computational algorithms targeted towards AR/VR Audio experience. You will be expected to build machine learning and computer vision pipelines to achieve these goals. Come join us as we make AR and VR happen!
Our internships are sixteen (16) to twenty four (24) weeks long and we have various start dates throughout the year.
- Design and develop novel learning and vision algorithms for Audio machine learning related research problems
- Exhaustively evaluate proposed designs and establish benchmarks
- Extensively collaborate with domain experts/researches across diverse disciplines including Audio,
- Acoustics and Hardware
- Communication of research agenda, progress and results.
- Pursuing a PhD in Computer Science, Electrical Engineering, or a related Applied Math field
- Currently enrolled in a full time degree program and returning to the program after the completion of the internship
- High levels of creativity and quick problem solving capabilities
- Interpersonal skills: cross-group and cross-culture collaboration
- Ability to obtain work authorization in the United States in 2018
- Strong research background in Machine Learning, Artificial Intelligence, Computer Vision or Statistical Signal Processing
- Background in metric learning or unsupervised learning or reinforcement/adaptive learning or 3d reconstruction
- Experience with machine learning libraries such as PyTorch, TensorFlow, etc.
- Proven track record of achieving results as demonstrated in accepted paper(s) at top computer vision and/or machine learning related conferences such as ICML, CVPR, ICCV, ECCV, NIPS, etc.
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