Applications for this job are now closed
Our Statistics & Privacy team focuses on establishing step-change improvements to advance Facebook’s long-term business growth. The team's expertise spans many domains including statistics, machine learning, economics, and cryptography.
We develop methodologies, design and prototype solutions, and partner with our engineering colleagues to launch these solutions such that millions of advertisers can benefit. We seek Research Interns to identify new opportunities and help build scientifically rigorous systems focused on enhancing technological guarantees for consumer privacy while simultaneously expanding the efficiency of Facebook’s market-leading advertising optimization systems.
Challenges and intern project topics include incorporating approaches such as differential privacy and multi-party computation within our ads delivery systems, designing machine learning systems on encrypted data and/or in decentralized or federated environments, and advancing the efficient frontier of privacy and utility in our ads systems.
About the role
You will be a Privacy-Preserving Machine Learning, Statistics & Privacy Research Intern who will be responsible for the following;
- Assess potential opportunities and execute world-class research associated with your area of scientific expertise
- Design and prototype new algorithms, optimization methods, and system architectures to advance our long-term priorities
- Establish approaches to rigorously assess validity and relative performance of new technologies or strategies
- Build cross-functional collaborations with AI, Engineering, Product and Analytics teams to shape roadmaps with a balance of scientific rigor and strategic considerations
- Learn new tools, systems, and programming languages quickly as required by the particular project you are working on
- Apply communication skills to engage diverse audiences on technical topics and nuanced insights
- Develop patent applications, white papers, and publications that are broadly appealing and accessible beyond a core scientific audience. Publish research results in top-tier journals and at leading conferences
About you
Minimum Qualifications
- Pursuing a PhD in computer science, electrical engineering, statistics, mathematics, or a related field
- Understanding of modern machine learning techniques and their mathematical underpinning
- Proficiency designing and implementing analytical and/or algorithmic solutions, tailored to particular business needs and tested on large data sets
- Proficiency in Python
- Experience communicating analysis and establishing confidence among audiences who do not share your disciplinary background or training
- Proven track record of innovation
- Expertise with at least one of the following: Secure Multi-Party Computation techniques, Differential Privacy, Federated Learning or Federated Analytics, Homomorphic Encryption, Trusted Execution Environments
- Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
- Must be currently enrolled in a full-time degree program and returning to the program after the completion of the internship/co-op
