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
Minimum Qualifications
• 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 Machine Learning, Artificial Intelligence, 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
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, 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
