Machine Learning Engineer — Multilingual Data
Featherless AI
If you want to work on a real problem early in your career, this is it. Most ML systems work well in English but fail in other languages and scripts. Featherless AI is building models that actually work across the world, and they need someone to own the data that makes that possible. This role puts you at the intersection of research, data, and production—where your decisions directly affect model quality.
You'll design and build large-scale multilingual datasets, develop pipelines for collection and cleaning, and implement quality filters using statistics and models. You'll work with researchers to define what success looks like across languages, analyze where datasets fail, and continuously improve them based on real performance. This isn't just labeling data—it's building the foundation that makes AI work beyond English.
This role is for someone with a strong foundation in data work: experience building pipelines, familiarity with Python and data tools, and genuine interest in how language and cultural context affect ML. A background in linguistics, computer science, or data science helps. A portfolio showing previous data or ML work strengthens your application.
To apply, submit your resume and a brief note about why multilingual ML matters to you on CareerJumpShip.
About this role
We’re looking for a Machine Learning Engineer to own and scale our multilingual data pipeline—from sourcing and curation to evaluation and continuous improvement. You’ll work closely with researchers and infra engineers to ensure our models perform robustly across languages, scripts, and cultural contexts. This role sits at the intersection of data, research, and production ML and is ideal for someone who cares deeply about data quality, linguistic diversity, and model generalization beyond English. 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 Continuously iterate on datasets based on model performance and real-world usage What We’re Looking For 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 Experience with low-resource languages or multilingual benchmarks (e.g. FLORES, XTREME) Exposure to LLM training, fine-tuning, or distillation Linguistics background or experience working with native language experts Contributions to open-source datasets or ML tooling Experience with data quality evaluation at scale Why Join Real ownership over a core differentiator of the product Work on models used globally, not just in English-speaking markets Small, high-caliber team with deep ML and systems experience Competitive compensation + meaningful equity at Series A stage Originally posted on Himalayas
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