Facebook Research Scientist - Privacy Preserving Machine Learning in London, United Kingdom
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.
Marketing Science R&D focuses on establishing step-change improvements to the efficiency of marketing on our platform. The team's expertise spans domains including causal inference, survey methodology, machine learning, cryptography, neuroscience and record linkage. We develop methodologies, design and prototype solutions, and partner with our engineering and product colleagues to scale these solutions such that millions of advertisers can benefit.
We seek research scientists — both new PhD graduates and industry-experience leaders — 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 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.
Identify specific, high-impact opportunities to build new and improve existing privacy-preserving advertising solutions
Design and prototype new algorithms, optimization methods, and architectures for privacy-preserving machine learning solutions
Build cross-functional relationships with AI, Engineering, Product and Analytics teams to shape long-term product roadmaps with a balance of scientific rigor and strategic considerations
Learn new tools, systems and languages quickly as required by the particular project you are working on
Develop patent applications, white papers, conference presentations, and publications that are broadly appealing and accessible beyond a core scientific audience
PhD in a quantitative field such as statistics, physics, operations research, computer science or mathematics, or MS/MA degree with 4+ years of relevant experience in data science or research science
Strong proficiency with Python
Deep understanding of modern machine learning techniques and their mathematical underpinning
- Expertise with at least one of the following privacy technologies: * Secure multi-party computation techniques for machine learning * Differential privacy * Federated Learning, optimization algorithms and protocols * Learning on encrypted data * Homomorphic encryption * Trusted execution environments
Proven track record of innovation
Industry experience with online advertising, optimization and online experimentation platforms
Experience developing and debugging in C/C++ and Java
Proficiency with Presto, Hive or Spark SQL