AI Researcher – Multilingual Data

About the Role We’re looking for an AI Researcher focused on multilingual data to help us build and scale next-generation language models across diverse languages and domains. You’ll own research and execution around data sourcing, curation, evaluation, and training strategies for multilingual and low-resource languages, with a strong emphasis on publishing high-quality research and translating it into production systems. This role is ideal for someone who enjoys working close to the frontier: balancing papers, prototypes, and real-world impact in a fast-moving startup environment. What You’ll Do • Design and execute research on multilingual datasets , including data collection, filtering, deduplication, and quality measurement • Develop strategies for low-resource and long-tail languages (sampling, augmentation, curriculum design) • Research and improve cross-lingual transfer , alignment, and robustness in large language models • Build and maintain evaluation benchmarks for multilingual performance • Collaborate with engineers and researchers on training pipelines and model architecture decisions • Publish research at top venues (e.g., ACL, EMNLP, NeurIPS, ICML, ICLR) and contribute to open-source when appropriate • Translate research insights into practical improvements in production models What We’re Looking For • Strong background in NLP / ML research , with a focus on multilingual or cross-lingual modeling • Publication record at respected conferences or journals (ACL, EMNLP, NeurIPS, ICML, ICLR, etc.) • Experience working with large-scale text datasets across multiple languages • Solid understanding of: • Tokenization and vocabulary design for multilingual models • Data quality metrics, filtering, and dataset bias • Transfer learning and multilingual representation learning • Comfortable prototyping in Python with modern ML frameworks (PyTorch, JAX, etc.) • Ability to operate independently and ship research in a startup pace environment Nice to Have • Experience with low-resource languages or non-Latin scripts • Open-source contributions in NLP or data tooling • Experience training or evaluating large language models • Familiarity with multilingual benchmarks (e.g., XTREME, FLORES, TyDi QA) Why Join Us • Real ownership over research direction and impact • A team that values papers and production • Access to meaningful scale: large datasets, modern infrastructure, and fast iteration • Competitive compensation and meaningful equity at an early stage Originally posted on Himalayas