Research Scientist Intern, Feed Recommendations (PhD) 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
Minimum Qualifications
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
Familiarity with algorithms behind generative model training and common deep learning architectures (Transformers, GPTs, BERT etc.)
Preferred Qualifications
Intent to return to the degree program after the completion of the internship/co-op
Proven track record of solid research achievements as demonstrated by grants, fellowships, patents, as well as publications at leading AI conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP and KDD
Prior research or project experience in one or more of the following areas: reinforcement learning, pre-training and supervised fine-tuning of large language models, sequence modeling, diffusion models, and their applications in recommendation systems
Demonstrated software development experience via tech internships, work experience, coding competitions, or widely used contributions in open source machine-learning repositories
Experience working and communicating cross functionally in a fast-paced team environment. Ideal candidates should have the ability to quickly understand and identify the research opportunities behind real-world applications, select the appropriate ML methods to explore, and proactively drive the iterations based on clear analysis of the current results
