Research Scientist

About the Role

The Monetization organization at Meta empowers people and businesses to succeed in the global economy by building valuable connections and premium services. As a Machine Learning Research Scientist on the Monetization team, you will help build ML/AI technologies that connect users with businesses, developing solutions that power large-scale platforms and AI innovations for Ads-ranking across Meta surfaces.

Key Responsibilities

• Develop highly scalable classifiers and tools leveraging Machine Learning, data regression, and rules-based models.
• Suggest, collect, and synthesize requirements to create an effective feature roadmap.
• Adapt standard Machine Learning methods to best exploit modern parallel environments (e.g., distributed clusters, multicore SMP, and GPU).
• Lead and contribute to research that results in tech demos and/or publications.
• Collaborate closely with cross-functional partners and contribute towards Meta's research product development.

Requirements & Tech Stack

• Currently has, or is in the process of obtaining a PhD degree in Machine Learning, Artificial Intelligence, or a relevant technical field. Degree must be completed prior to joining Meta.
• Experience in Deep Learning algorithms and techniques, e.g., convolutional neural networks (CNN), transformers, quantization, data efficient learning, or similar.

Qualifications

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field. Degree must be completed prior to joining Meta.
• 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, ICLR, AAAI, or similar.
• Experience on Data Efficient Learning, domain adaptation, Semi-supervised Learning, etc.
• Experience working and communicating cross-functionally in a team environment.
• Exposure to architectural patterns of large scale software applications.
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and varied points of view to determine a path forward.
• Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g., GitHub).
• Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.