Job Title: Machine Learning Engineer — Multilingual Data

Company Name: Featherless

Job Details: RemoteFull,Time

Job Url: https://hiring.cafe/viewjob/d8stekrsaon7o2z1

Job Description: Posted 1mo agoMachine Learning Engineer — Multilingual Data@ FeatherlessView All JobsWebsiteUnited StatesRemoteFull TimeResponsibilities:design datasets, build pipelines, evaluate metricsRequirements Summary:3+ years in ML/AI roles; experience with multilingual or non-English data; NLP fundamentals; scalable data pipelines; Unicode and language quirks; collaboration with researchers.Technical Tools Mentioned:Python, Spark, Ray, NLP
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 DoDesign, build, and maintain large-scale multilingual datasets across high- and low-resource languagesDevelop data pipelines for collection, cleaning, normalization, deduplication, and labelingImplement quality filters using statistical, heuristic, and model-based methodsWork with researchers to define language coverage, benchmarks, and evaluation metricsAnalyze dataset bias, coverage gaps, and failure modes across regions and scriptsSupport training, fine-tuning, and distillation workflows with high-quality multilingual dataContinuously iterate on datasets based on model performance and real-world usageWhat We’re Looking For3+ years of experience as an ML Engineer, Applied Scientist, or similar roleStrong experience working with multilingual or non-English datasetsSolid 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 quirksComfort collaborating with researchers and translating research needs into production systemsNice to HaveExperience with low-resource languages or multilingual benchmarks (e.g. FLORES, XTREME)Exposure to LLM training, fine-tuning, or distillationLinguistics background or experience working with native language expertsContributions to open-source datasets or ML toolingExperience with data quality evaluation at scaleWhy JoinReal ownership over a core differentiator of the productWork on models used globally, not just in English-speaking marketsSmall, high-caliber team with deep ML and systems experienceCompetitive compensation + meaningful equity at Series A stage