Research Scientist Intern – Monetization Generative AI – LLM – Language – PhD

Research Scientist Intern – Monetization Generative AI – LLM – Language – PhD

Requirements

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, Sentiment Analysis, Computer Vision, or relevant technical field.
• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment.
• Experience with Python, C++, C, Java or other related language.
• Experience with deep learning frameworks such as Pytorch or Tensorflow.
• Experience building systems based on machine learning, deep learning methods, or natural language processing.

Responsibilities

• Perform research to advance the science and technology of intelligent machines.
• Develop novel and accurate NLP 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.

Preferred Qualifications

• Intent to return to degree program after the completion of the internship/co-op.
• 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, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar.
• Experience with ML areas such as Natural Language Processing, Speech, Multimodal Reasoning & Retrieval, Visual Question & Answering.
• Experience with Large Language Models, on top of the latest research of text only input and multimodal input to large language models.
• 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 the development of enterprise level AI, Machine Learning, and Deep Learning platform involving big data management and GPU compute.
• Experience building systems based on machine learning, reinforcement learning and/or deep learning methods.
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
• Experience working and communicating cross functionally in a team environment.