Research Scientist, Machine Learning for Monetization (PhD)

From making valuable connections between people and businesses to building premium services that deliver high-value experiences, the Monetization organization at Meta empowers people and businesses to succeed in the global economy. As Meta focuses on building the next evolution of social experiences, the Monetization team plays a crucial role in shaping the communication pathways and financial tools that all sized businesses need to thrive in the new digital economic environment. As a Machine Learning Research Scientist on the Monetization team at Meta, you can help build ML/AI technologies that connect users with businesses You’ll help develop solutions that power 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 research that results in tech demos and/or publications
Collaborate closely with cross-functional partners and contribute towards Meta's research product development

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 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