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Research Scientist in Code Generation
Il y a 3 mois
Meta is on the lookout for a Postdoctoral Researcher specializing in code generation utilizing Large Language Models (LLMs) at the Fundamental AI Research (FAIR) division. Our team is dedicated to pioneering research and producing influential publications, as well as developing frameworks and libraries that contribute significantly to the open-source ecosystem.
This role involves collaborating with various teams within Meta to translate cutting-edge research into practical applications. The focus will be on exploring novel methodologies for code analysis and generation through innovative LLM architectures. Notable past projects include Code Llama. The primary objective is to engineer state-of-the-art algorithms that emphasize open research and scientific innovation.
Key Responsibilities- Conduct research aimed at addressing unresolved challenges in code generation with LLMs, particularly in handling long-context inputs and generating long-form outputs.
- Independently conceptualize and execute algorithms, train advanced models on extensive datasets, and assess their effectiveness.
- Prepare scholarly articles and refine them based on feedback from peers, mentors, and reviewers.
- Currently pursuing or in the process of completing a PhD in Artificial Intelligence, Machine Learning, Natural Language Processing, or possess equivalent practical experience.
- Research background in one or more of the following domains: LLMs, code analysis and generation, machine translation, machine learning, deep learning, or related fields.
- Familiarity with machine learning frameworks such as Pytorch and TensorFlow.
- Understanding of deep learning principles and neural networks.
- Proficiency in scripting languages like Python and shell scripting.
- Must secure work authorization in the employment country upon hiring and maintain it throughout employment.
- Experience in developing scalable machine learning models in areas such as code analysis and generation with LLMs, unsupervised/semi-supervised training, or related fields.
- Experience with large-scale model training, algorithm implementation, and evaluation of LLMs.
- A proven history of impactful contributions as evidenced by publications in leading conferences like NeurIPS, ICLR, ACL, EMNLP, or similar.
