Machine Learning Engineer — Multilingual Data
About the role
Featherless AI is hiring a Machine Learning Engineer to own and scale its multilingual data pipeline, from sourcing and curation through evaluation. The role sits between data, research, and production ML so models hold up across languages, scripts, and cultural contexts. It is a full-time remote (worldwide) role.
What you'll do
- Design, build, and maintain large-scale multilingual datasets across high- and low-resource languages.
- Develop data pipelines for collection, cleaning, normalization, deduplication, and labeling.
- Implement quality filters using statistical, heuristic, and model-based methods.
- Work with researchers to define language coverage, benchmarks, and evaluation metrics.
- Analyze dataset bias, coverage gaps, and failure modes across regions and scripts.
- Support training, fine-tuning, and distillation workflows with high-quality multilingual data.
- Iterate on datasets based on model performance and real-world usage.
Requirements
- 3+ years of experience as an ML Engineer, Applied Scientist, or similar role.
- Strong experience working with multilingual or non-English datasets.
- Solid understanding of NLP fundamentals (tokenization, embeddings, language modeling).
- Experience building scalable data pipelines (Python, Spark, Ray, or similar).
- Familiarity with Unicode, scripts, tokenization challenges, and language-specific quirks.
- Comfort collaborating with researchers and translating research needs into production systems.
- Nice to have: low-resource languages or multilingual benchmarks (e.g. FLORES, XTREME); LLM training, fine-tuning, or distillation; linguistics background; open-source dataset or ML-tooling contributions.
About Featherless AI
Featherless AI builds and serves machine-learning models, with a focus on architecture research, multilingual data, and efficient training and inference.
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