Machine Learning Engineer — Multilingual Data
At a glance
Mid-level Machine Learning Engineering role at Featherless AI. Remote · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain large multilingual datasets, from finding source material through curation and evaluation
- Create pipelines that collect, clean, normalize, deduplicate and label multilingual data
- Use statistical, heuristic and model-based checks to catch quality problems in datasets
- Work with researchers on language coverage and benchmarks, then investigate bias, gaps and failures across regions and scripts
- Provide data for training, fine-tuning and distillation, revising datasets as model results and real-world use change
What you bring
- At least three years as an ML engineer, applied scientist or in a similar position
- Strong practical experience with multilingual or non-English datasets
- A solid grasp of NLP foundations, including tokenization, embeddings and language modeling
- Experience creating scalable data pipelines with Python, Spark, Ray or another listed tool
- Familiarity with Unicode, scripts, tokenization difficulties and language-specific quirks
Who this fits
This remote, mid-level role suits an ML engineer who wants to connect research needs with production data systems. You’ll work with researchers and infrastructure engineers on model performance across languages, scripts and cultural contexts.
From the employer
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