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 and fine-tuning of models to achieve superior results on various NLP tasks and benchmarks.

Job Specifications

Academic Qualification

A Master’s degree or Ph.D. in Computer Science, Computational Linguistics, Statistics, Machine Learning, or a related field with a focus on NLP.

Professional Experience

Essential

• At least three years of strong expertise in Natural Language Processing (NLP) with a deep understanding of large language models and advanced NLP techniques.
• Deep Learning: Proficiency in developing and optimizing deep learning architectures for NLP tasks, such as transformers, recurrent neural networks, and sequence-to-sequence models.
• Programming: Excellent coding skills in Python, with the ability to write efficient, modular, and well-documented code for NLP algorithms and models.
• Machine Learning: Solid knowledge of machine learning techniques, including supervised and unsupervised learning, transfer learning, and reinforcement learning, to enhance NLP models.
• Research Experience: Demonstrated experience in conducting research, publishing papers, and contributing to NLP-related patents or publications.
• Large Language Models: Experience in building, fine-tuning, and evaluating large language models like BERT, GPT, etc.
• Data Preprocessing: Proficiency in data preprocessing techniques and tools for cleaning, transforming, and annotating text data for NLP tasks.
• Evaluation and Optimization: Experience in designing evaluation methodologies and optimizing NLP models to achieve superior performance on various benchmarks.