AI Researcher – Multilingual Data

About the Role

We’re looking for anAI 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 arounddata sourcing, curation, evaluation, and training strategies for multilingual and low-resource languages, with a strong emphasis onpublishing high-quality research and translating it into production systems.

This role is ideal for someone who enjoys working close to the frontier: balancingpapers, prototypes, and real-world impact in a fast-moving startup environment.

What You’ll Do

• Design and execute research onmultilingual datasets, including data collection, filtering, deduplication, and quality measurement

• Develop strategies forlow-resource and long-tail languages (sampling, augmentation, curriculum design)

• Research and improvecross-lingual transfer, alignment, and robustness in large language models

• Build and maintainevaluation benchmarks for multilingual performance

• Collaborate with engineers and researchers ontraining 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 intopractical improvements in production models

What We’re Looking For

• Strong background inNLP / 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 withlarge-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 inPython with modern ML frameworks (PyTorch, JAX, etc.)

• Ability to operate independently and ship research in astartup pace environment

Nice to Have

• Experience withlow-resource languages or non-Latin scripts

• Open-source contributions in NLP or data tooling

• Experience training or evaluatinglarge language models

• Familiarity with multilingual benchmarks (e.g., XTREME, FLORES, TyDi QA)

Why Join Us

• Real ownership overresearch direction and impact

• A team that valuespapersand production

• Access to meaningful scale: large datasets, modern infrastructure, and fast iteration

• Competitive compensation and meaningful equity at an early stage