Research Scientist, Applied Perception Science (PhD)

We are seeking a Research Scientist with expertise at the intersection of human and machine perception excited about pushing the boundaries of our understanding of vision to improve design of sensor-to-display control pipelines for augmented reality products.
Unlike standard 2D displays designed for relatively static viewers and environments, AR displays must adapt dynamically to user behavior and their environments to ensure seamless blending of virtual and real world objects. Achieving such dynamic adaptation requires advancing definitions and implementation of models of human perception beyond sensory encoding limitations that have found success for more traditional displays. With this challenge in mind, we seek researchers with expertise in dimensionality reduction methods, modern DeepNet architectures and representation learning to develop and test novel computational perceptual spaces that serve to establish core requirements for automatic display control for AR glasses.
Reality Labs (RL) brings together world-class, cross-disciplinary science and engineering teams with the shared goal of developing the next generation of AI for AR technologies. In this role, you will be embedded in a multidisciplinary team of scientists and engineers exploring innovative approaches to using data from egocentric cameras and motion detectors (IMU) and knowledge about human visual processes to model image encoding by the eye and applying those models to predict the AR display image quality. Projects take advantage of internal resources in mechanical, electrical, optical, and software engineering, allowing us to confront scientific and product engineering challenges which cannot necessarily be answered with existing products or prototypes.Research Scientist, Applied Perception Science (PhD) Responsibilities

• Identify, develop, implement, and evaluate methods for learning robust representations to ground models human visual performance from egocentric video data
• Learn, evaluate, and use color and spatially calibrated camera and image processing pipeline to estimate physical scene parameters (e.g. luminance, chromaticity, 3D geometry as a function of visual direction)
• Utilize Meta’s large infrastructure to scale and speed up experimentation
• Write modular research code that can be reused in other contexts.
• Collaborate with experts in image quality computer and human vision

Minimum Qualifications

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Currently has or is in the process of pursuing a PhD in Machine Learning, Computer Vision, Vision Science, Applied Statistics, Computational Neuroscience, or a related field. Degree must be completed prior to joining Meta
• Proficiency in python and machine learning libraries (numpy, scikit-learn, scipy, pandas, matplotlib, tensorflow, pytorch)
• 2+ Years of experience in approaches to using self-supervised learning for computer vision including DeepMetric learning / neural network embedding methods
• Experience in understanding of approaches to characterize the statistics of natural images and/or how to use properties of network layers to characterize high level scene/image properties
• Experience with research involving defining problems, exploring solutions, and analyzing and presenting results
• Experience in communicating effectively with a broad range of stakeholders, collaborators and clients, at different levels
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications

• Understanding of color image processing pipelines
• Understanding of modern adaptive camera and image processing pipelines
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences
• Experience working and communicating cross-functionally in a team environment

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