Research Scientist Intern, Neuroscience / NLP 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 Masters degree (or equivalent) in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, Neuroscience, 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.
- Experience with deep learning frameworks such as Pytorch.
- Experience building systems based on machine learning, deep learning methods, or natural language processing.
- Experience in high-performance computing.
- Experience in collaborative coding (Github).
Preferred Qualifications:
- Intent to pursue a PhD 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, Neuroscience.
- 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).
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