Multilingual ML Engineer: Data Pipelines & Datasets

Featherlessai; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Featherlessai in the United States seeks an ML Engineer to design, build, and maintain large-scale multilingual datasets across high- and low-resource languages, aligning data with research goals.

You will develop scalable data pipelines (Python, Spark, Ray), apply quality filters, and collaborate with researchers to define language coverage, benchmarks, and evaluation metrics while addressing bias and regional/script variation in data.

Qualifications

• 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.
• Ability to collaborate with researchers and translate research needs into production systems.

Responsibilities

• 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.

Skills

ML Engineer

Multilingual data

NLP basics

Data pipelines

Unicode/tokenization

Research collaboration

Tools

Python

Spark

Ray

Job description

Featherlessai in the United States seeks an ML Engineer to design, build, and maintain large-scale multilingual datasets across high- and low-resource languages, aligning data with research goals.

You will develop scalable data pipelines (Python, Spark, Ray), apply quality filters, and collaborate with researchers to define language coverage, benchmarks, and evaluation metrics while addressing bias and regional/script variation in data.