Postdoctoral Researcher, FAIR

Key Skills

Aartificial intelligence, Analytical and Problem solving, C Programming, C++, Computer Vision (CV), Data science techniques, Deep Learning, Effective communication skills, Java Programming, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), NeurIPS, Python Programming, PyTorch

Job Description

Postdoctoral Researcher, FAIR Responsibilities

• Perform research to push the scientific and technological frontiers of multimodal generative modeling.
• Invent/improve large-scale data-driven paradigms for multimodal generation.
• Enable reasoning with multimodal understanding and generation systems.
• Develop the next generation of platforms for AI & multimodal research.

Minimum Qualifications

• Currently has or is in the process of obtaining a PhD degree in the field of Machine Learning, Speech and Audio Processing, Multimodal Understanding and Generation, Generative Modeling, a related field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Experience in training and use of large language models or diffusion models
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading ML conferences (NeurIPS, ICML, ICLR), Speech (Interspeech, ICASSP), and/or Computer Vision (CVPR, ECCV, ICCV)
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Experience in deep learning frameworks (such as PyTorch, TensorFlow), C, C++, Python.
• Experience in AI frameworks like Hugging Face, fairsesq or other systems.

Preferred Qualifications

• Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
• Experience building systems based on machine learning and/or deep learning methods.
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
• Experience working and communicating cross functionally in a team environment.
• Research experience in machine learning, representation learning, optimization, statistics, applied mathematics, or data science.