AI Research Scientist, VLLM (Vision Large Language Models) - Generative AI

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 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., AI Research Scientist, VLLM (Vision Large Language Models) - Generative AI Responsibilities:

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

1. Experience in Deep Learning algorithms and techniques, e.g., convolutional neural networks (CNN), transformers, quantization, data-efficient learning, or similar
2. Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment

Preferred Qualifications:

Preferred Qualifications:

1. Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
2. Experience in data efficient learning, domain adaptation, semi-supervised learning, etc
3. Experience working and communicating cross-functionally in a team environment
4. 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
5. Exposure to architectural patterns of large-scale software applications
6. Experience in manipulating and analyzing complex, high-volume, high-dimensionality data from various sources
7. Experience solving complex problems and comparing alternative solutions, trade-offs, and varied points of view to determine a path forward