Research Scientist Intern, XR Data Ingestion Systems (PhD)

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 - we're just getting started.


Our team is responsible for maintaining the quality and infrastructure of petabyte scale video and image anonymization.Pseudonymization is replacing actual human faces in training data with synthetic faces. We need to invest in this area since training data for anonymization models must not not contain any PII. We have an option to either blur the training data and hope the net recognizes body parts to blur the face OR we pseudonymize the data thus eliminating any privacy concerns. The internship will objectively measure both approaches.Impact:Currently, the anonymization model teams are severely impacted because of strict privacy requirements for our training data. With a stagnant test+training dataset we are lacking the ability to improve the model. We'd like to record all known misses of the model and use it to improve training data - but can't today. If pseudonymization or blurred faces proves to only mildly affect precision and recall (mean average precision across the two), we will be able to unlock the ability to have very accurate anonymization models that work for bespoke domains.

Required Skills

  • Comparative analysis of how blurring faces and pseudonymization in training data affect precision and recall on a golden dataset.
  • Create a system (or use handcrafted data) that can replace instances in an image with synthetic faces.

Minimum Qualification

  • Currently has, or is in the process of obtaining, a PhD degree in Computer Science, Electrical Engineering, or Electrical and Computer Engineering in the field of computer vision, machine learning, computer graphics, or robotics.
  • 3 years experience with systems building in C++ and Python.
  • 2 years experience with modern deep learning frameworks like PyTorch.
  • Knowledge in computer vision, machine learning, image processing, object recognition or object tracking.
  • Proven track record of achieving significant results as demonstrated by publications in top computer vision conferences (e.g., CVPR, ICCV, ECCV, or, SIGGRAPH) or journals (e.g., IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Pattern Recognition, or IEEE Transactions on Image Processing).
  • High levels of creativity and quick problem solving capabilities.
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.

Preferred Qualification

  • Demonstrated software engineering experience via an internship, work experience, coding competitions, open source contributions, or research.
  • Ability to communicate complex research in a clear, precise, and actionable manner.
  • Intent to return to degree-program after the completion of the internship.


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