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