Featherless AI
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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
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