Visiting Researcher, Methods/Models for Human Perception Responsibilities at Meta (formerly Facebook/Oculus)

Link: https://www.facebookcareers.com/v2/jobs/200816258794061/

Visiting Researcher, Methods/Models for Human Perception Responsibilities

  • Advance the state-of-the-art in human-in-the-loop experimentation for perception and perceptually-informed outcomes.

  • Consult and collaborate with partner and client teams to deploy experimentation tooling in real experiments and experiences.

Minimum Qualifications

  • Currently has, or is in the process of obtaining, a Ph.D. degree in psychology, cognitive science, neuroscience, machine learning, statistics, data science, or a related area, or equivalent work experience.

  • 2+ years research experience in an area with potential applications to human-in-the-loop experimentation in human perception and related domains. Example areas include computational cognitive science, computational neuroscience, psychophysics, active learning, deep Bayesian learning, Gaussian Process models, hierarchical/multitask models, or other relevant areas, as demonstrated via publications (conference or journal), open-source contributions, or similar venues.

  • 1+ years of experience communicating and collaborating with other researchers, as demonstrated by collaborative projects or co-authored research presentations, material (publications/blog posts), or similar.

  • Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

Preferred Qualifications

  • Experience with crossing disciplinary boundaries, for example from machine learning and statistics to cognitive science, from applied mathematics to neuroscience, or similar, as demonstrated by collaborations across disciplinary domains.

  • Experience creating research software used by others, for example as part of an academic collaboration, an open-source project, or equivalent.

  • Experience with SciPy/NumPy and at least one automatic differentiation framework (PyTorch, TensorFlow, JAX, Stan, etc.) -- PyTorch preferred.

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