{"id":694772,"url":"https://alion.io/job/featherless-machine-learning-engineer-multilingual-data","title":"Machine Learning Engineer — Multilingual Data","company":{"id":673367,"name":"Featherless","domain":"featherless.ai","url":"https://alion.io/company/featherless","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"C","score":60,"open_postings":16,"ghost_share":0.75,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":7,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Spark","optional":false},{"name":"Tokenization","optional":false},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-01-22T05:07:38Z","employer_posted_date":"2026-01-22","last_verified_at":"2026-10-31T10:16:39Z","board_verified":true,"closed_at":null,"days_open":282,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":282},"description":"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.\nThis 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.\nWhat You’ll Do\nDesign, build, and maintain large-scale multilingual datasets across high- and low-resource languages\n\nDevelop data pipelines for collection, cleaning, normalization, deduplication, and labeling\n\nImplement quality filters using statistical, heuristic, and model-based methods\n\nWork with researchers to define language coverage, benchmarks, and evaluation metrics\n\nAnalyze dataset bias, coverage gaps, and failure modes across regions and scripts\n\nSupport training, fine-tuning, and distillation workflows with high-quality multilingual data\n\nContinuously iterate on datasets based on model performance and real-world usage\n\nWhat We’re Looking For\n3+ years of experience as an ML Engineer, Applied Scientist, or similar role\n\nStrong experience working with multilingual or non-English datasets\n\nSolid understanding of NLP fundamentals (tokenization, embeddings, language modeling)\n\nExperience building scalable data pipelines (Python, Spark, Ray, or similar)\n\nFamiliarity with Unicode, scripts, tokenization challenges, and language-specific quirks\n\nComfort collaborating with researchers and translating research needs into production systems\n\nNice to Have\nExperience with low-resource languages or multilingual benchmarks (e.g. FLORES, XTREME)\n\nExposure to LLM training, fine-tuning, or distillation\n\nLinguistics background or experience working with native language experts\n\nContributions to open-source datasets or ML tooling\n\nExperience with data quality evaluation at scale\n\nWhy Join\nReal ownership over a core differentiator of the product\n\nWork on models used globally, not just in 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