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💼 Job

Research Scientist, Central Applied Science, Privacy-Preserving ML (PhD)

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11d ago

💼 Graduate Job

San Francisco +2

AI generated summary

  • The ideal candidate for the Research Scientist position at Meta must have a Bachelor's degree in Computer Science or relevant field, as well as a PhD in Computer Science, Statistics, or related field. They should be skilled in designing and implementing analytical and algorithmic solutions, with experience in technologies like Differential Privacy or Federated Learning. Proficiency in Python is a must, and C/C++ proficiency is preferred. Strong communication and teamwork skills are also essential.
  • The candidate will be responsible for conducting research on privacy-preserving ML techniques, designing and prototyping new algorithms, implementing and deploying them, and conducting empirical studies to showcase their value in real-world applications.

Graduate Job

DataSan Francisco, Seattle, New York


  • We seek Research Scientists to identify new opportunities and help build scientifically rigorous systems focused on enabling new capabilities and improved performance. This role focuses on Meta’s large scale machine learning and analytics systems for Meta's products across Facebook, Instagram, WhatsApp, Messenger, and Reality Labs.
  • Our team's applied research includes incorporating approaches such as differential privacy for large scale model training, designing effective attacks, and evaluating practical risks in various applications including recommendation systems and generative models. We also pursue advancements in federated learning and on-device processing, where client-side applications include recommender systems for advertising and organic content, generative AI experiences, models, text prediction, and scene understanding.


  • 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 obtaining, a PhD degree in Computer Science, Statistics, Mathematics, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
  • Proficiency designing and implementing analytical and/or algorithmic solutions, tailored to particular business needs and tested on large data sets.
  • Proficiency with at least one of the following technologies: (i) Differential Privacy, (ii) Privacy Attacks and Auditing for ML model risks, (iii) Federated Learning, (iv) On-Device Model Personalization.
  • Proficiency in Python.
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
  • Preferred Qualifications:
  • 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 NeurIPS, ICML, AISTATS, MobiCom, MobiSys, ICLR, etc.
  • Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
  • Experience in training, fine-tuning, and/or experimenting with foundation models beyond black-box use.
  • Familiarity with one or more deep learning frameworks (e.g. PyTorch, TensorFlow, etc.).
  • Proficiency in C or C++.
  • Experience working and communicating cross-functionally in a team environment.

Education requirements

Currently Studying

Area of Responsibilities



  • Assess potential opportunities and execute world-class research associated with privacy-preserving techniques, federated learning, and on-device personalization.
  • Design and prototype new algorithms or mechanisms, optimization methods, and system architectures.
  • Derive theoretical formulations when necessary.
  • Implement new algorithms or mechanisms, deploy them into internal or open-sourced libraries and tooling platforms, and conduct empirical studies on internal datasets to showcase value for real world applications.


Work type

Full time

Work mode



San Francisco, Seattle, New York