Applied Scientist – NLP

Job Purpose

As an Applied Scientist specializing in Natural Language Processing (NLP) with a focus on large language models and deep learning, your role will be crucial in advancing cutting-edge language processing technologies and contributing to the development of intelligent systems. You will be responsible for a wide range of tasks encompassing research, development, and implementation of NLP solutions, with a particular emphasis on Python coding
, machine learning techniques
, and deep learning methodologies
.

Paris, Abu Dhabi or [Silicon Valley]

Successful applicants may choose to work at MBZUAI or Inception (a G42 company) as per mutual agreements.

• Conduct extensive research on state-of-the‑art NLP techniques
  , large language models
  , and deep learning approaches to solve complex language understanding tasks.
• Collaborate with cross-functional teams to innovate and develop novel algorithms and models that push the boundaries of NLP capabilities.

• Design, build, and optimize large-scale language models such as BERT
  , GPT
  , etc., with a focus on achieving superior performance on various NLP benchmarks and real-world applications.

• Implement efficient data preprocessing pipelines to clean, preprocess, and annotate text data for training large language models and machine learning models, ensuring data quality and suitability for training tasks.

• Develop and improve deep learning architectures for NLP tasks, including sequence-to-sequence models
  , transformers
  , recurrent neural networks
  , reinforcement learning
  , and other state-of-the‑art neural network structures.

• Write robust, modular, and scalable code to implement NLP algorithms
  , frameworks, and libraries, ensuring code readability, maintainability, and adherence to coding standards.

• Apply a diverse set of machine learning techniques
  , such as supervised and unsupervised learning
  , transfer learning
  , and reinforcement learning
  , to improve NLP models’ performance and versatility.

• Design rigorous evaluation methodologies to assess the performance of NLP models.
• Conduct extensive experiments.