Source description
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
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Design and execute research on multilingual datasets, including data collection, filtering, deduplication, and quality measurement
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Develop strategies for low-resource and long-tail languages (sampling, augmentation, curriculum design)
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Research and improve cross-lingual transfer, alignment, and robustness in large language models
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Build and maintain evaluation benchmarks for multilingual performance
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Collaborate with engineers and researchers on training pipelines and model architecture decisions
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Publish research at top venues (e.g., ACL, EMNLP, NeurIPS, ICML, ICLR) and contribute to open-source when appropriate
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Translate research insights into practical improvements in production models
What We’re Looking FOR
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Strong background in NLP / ML research, with a focus on multilingual or cross-lingual modeling
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Publication record at respected conferences or journals (ACL, EMNLP, NeurIPS, ICML, ICLR, etc.)
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Experience working with large-scale text datasets across multiple languages
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Solid understanding of:
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Tokenization and vocabulary design for multilingual models
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Data quality metrics, filtering, and dataset bias
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Transfer learning and multilingual representation learning
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Comfortable prototyping in Python with modern ML frameworks (PyTorch, JAX, etc.)
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Ability to operate independently and ship research in a startup pace environment
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NICE TO HAVE
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Experience with low-resource languages or non-Latin scripts
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Open-source contributions in NLP or data tooling
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Experience training or evaluating large language models
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Familiarity with multilingual benchmarks (e.g., XTREME, FLORES, TyDi QA)
WHY Join US
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Real ownership over research direction and impact
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A team that values papers and production
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Access to meaningful scale: large datasets, modern infrastructure, and fast iteration
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Competitive compensation and meaningful equity at an early stage
More at Featherless AI
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