Research Scientist Intern, Large Multimodal Models (PhD)

We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, audio and speech processing, natural language processing and multimodal reasoning. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Large Multimodal Models (PhD) Responsibilities:

• Perform research to advance the science and technology of intelligent machines.
• Develop novel and accurate multimodal algorithms and systems, leveraging Deep Learning and Machine Learning on big data resources.
• Analyze and improve efficiency, scalability, and stability of various deployed systems.
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Publish research results and contribute to research that can be applied to Meta product development.

Minimum Qualifications:

• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
• Experience with Python, C++, C, Java or other related languages.
• Experience with deep learning frameworks such as Pytorch or Tensorflow.
• Experience building systems based on machine learning, deep learning methods, or natural language processing.

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, ICLR, ICML, ACL, NAACL, EMNLP, or similar.
• Experience with ML areas such as Natural Language Processing, Speech, Multimodal Reasoning & Retrieval.
• Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
• Experience with training deep neural networks for key NLP tasks.
• Experience with interpreting deep neural networks mechanistically, correlating their observable behavior with properties of model parameters and activations.
• Experience working and communicating cross functionally in a team environment.

Source ⇲