As a Machine Learning Research Scientist on the Monetization team at Meta, you can help build ML/AI technologies that can effectively connect users with businesses You'll help develop solutions that power next-generation, large-scale platforms and AI innovations to power the Ads-ranking for Meta-scale across all the Meta surfaces.
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 cutting-edge research that results in industry-leading tech demos and/or publications
• Collaborate closely with cross-functional partners and contribute to Meta's research product development
Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Currently has, or is in the process of obtaining, a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, or a 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 Machine Learning, Artificial Intelligence, a relevant technical field, or equivalent practical experience. 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
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
Preferred Qualifications
• 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 in data efficient learning, domain adaptation, semi-supervised learning, etc
• Experience working and communicating cross-functionally in a team environment
• 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
• Exposure to architectural patterns of large-scale software applications
• Experience in manipulating and analyzing complex, high-volume, high-dimensionality data from various sources
• Experience solving complex problems and comparing alternative solutions, trade-offs, and varied points of view to determine a path forward
