AI Researcher – Multilingual Data

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.

• 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

• 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

• 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)

• 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