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.

Location

Paris, Abu Dhabi or [Silicon Valley]

Affiliation

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

Key Responsibilities

Research and development

• 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.

Large language model development

• 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.

Data preprocessing and annotation

• 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.

Deep learning architecture

• 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.

Coding

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

Machine learning algorithms

• 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.

Model evaluation and optimization

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