AI Researcher – Multilingual Data

About the role

Join a cutting‑edge research environment in the United Arab Emirates where multilingual AI innovation meets real‑world impact. As an AI Researcher – Multilingual Data you will lead the creation of high‑quality multilingual datasets and develop research strategies that power next‑generation language models across diverse languages and domains.

Key responsibilities

• Design and conduct research on multilingual datasets, including collection, filtering, deduplication, quality assessment and optimization.
• Develop innovative strategies for low‑resource and long‑tail languages using advanced sampling, data augmentation and curriculum learning.
• Improve multilingual large language models by enhancing cross‑lingual transfer, alignment, robustness and representation learning.
• Build, maintain and refine multilingual evaluation benchmarks to measure model performance across languages.
• Collaborate with machine‑learning engineers and researchers to influence training pipelines, model architectures and production deployment.
• Publish research findings at leading AI/NLP conferences and contribute to open‑source initiatives.
• Translate research outcomes into practical improvements for production‑ready AI systems.

Required profile

• Advanced background in Natural Language Processing, Machine Learning or Artificial Intelligence.
• Proven research experience in multilingual or cross‑lingual language modelling with publications at conferences such as ACL, EMNLP, NeurIPS, ICML or ICLR.
• Hands‑on experience with large‑scale multilingual text datasets and modern ML workflows.
• Strong understanding of multilingual tokenisation, vocabulary design, transfer learning, dataset quality assessment, filtering techniques and bias mitigation.
• Ability to work independently, manage research initiatives and deliver high‑quality results in a fast‑moving startup environment.

Required skills

• Python programming.
• Deep‑learning frameworks: PyTorch or JAX.
• Multilingual tokenisation and vocabulary design.
• Transfer learning and multilingual representation learning.
• Dataset quality assessment, filtering and bias mitigation.
• Experience with low‑resource languages and non‑Latin scripts.
• Familiarity with multilingual evaluation benchmarks (XTREME, FLORES, TyDi QA) and open‑source NLP projects.