Facebook Reality Labs (FRL) brings together a world-class, cross-discipline R&D team with the shared goal of developing the next generation of AR and VR technologies. The Surreal Vision team is looking for the next generation of scientists and engineers to tackle the most ambitious problems in machine perception at our research location in Redmond, WA.
The focus of this internship project lies in formulating a new generation of end-to-end real-time 3D scene understanding systems working across novel mobile sensor design, state estimation, computer vision, reconstruction, object-tracking, human-pose-tracking and large-scale online inference and optimization.
Our internships are twelve (12) to twenty four (24) weeks long and we have various start dates throughout the year.
* Research across Machine Perception at the intersections of Deep Learning, reconstruction and state estimation
Design and execution of algorithms
Prototyping, building and analysis of experimental systems
Collaboration with and support of other researchers across various disciplines
Communication of research agenda, progress and results
* Must be currently enrolled in a full-time degree program and returning to the program after the completion of the internship/co-op
Pursuing a PhD degree in the field of Computer Science, Computer Vision, Machine Learning or related field
2+ years experience with statistical analysis of data and mathematical modeling
2+ years experience with developing machine learning, Bayesian filtering, information theory, 3D geometry, optimization, system building, and physics based simulation
2+ years experience in C++, Java or Python. Familiarity with deep learning frameworks such as Pytorch and TensorFlow
Interpersonal experience: cross-group and cross-culture collaboration
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Must be available for 3 month to 6 month internship between January 1, 2019 and December 31, 2019
* Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as CVPR, ECCV/ICCV, SIGGRAPH, NIPS, etc.
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