机器学习工程师 — 多语言数据
Machine Learning Engineer — Multilingual Data
我们正在寻找一名机器学习工程师，负责构建和扩展我们的多语言数据流水线——从数据获取和整理到评估和持续改进。你将与研究人员和基础设施工程师紧密合作，确保我们的模型在多种语言、文字和文化背景下都能稳定运行。
该职位位于数据、研究和生产机器学习的交汇点，适合对数据质量、语言多样性以及超越英语的模型泛化能力有深刻理解的人。
你将负责
· 在高资源和低资源语言中设计、构建和维护大规模多语言数据集
· 开发用于收集、清洗、标准化、去重和标注的数据流水线
· 使用统计方法、启发式方法和基于模型的方法实现质量过滤
· 与研究人员合作定义语言覆盖范围、基准测试和评估指标
· 分析不同地区和文字中的数据集偏差、覆盖缺口和失败模式
· 使用高质量的多语言数据支持训练、微调和模型压缩流程
· 根据模型表现和实际使用情况持续迭代数据集
我们希望你具备
· 3年以上机器学习工程师、应用科学家或类似角色的经验
· 在多语言或非英语数据集方面有丰富经验
· 对自然语言处理基础有扎实理解（分词、嵌入、语言建模）
· 有构建可扩展数据流水线的经验（Python、Spark、Ray 或类似工具）
· 熟悉 Unicode、文字系统、分词挑战和语言特定的细节
· 能够与研究人员协作，并将研究需求转化为生产系统
加分项
· 在低资源语言或多语言基准测试（如 FLORES、XTREME）方面有经验
· 有大型语言模型训练、微调或压缩的经验
· 有语言学背景或与母语专家合作的经验
· 有开源数据集或机器学习工具的贡献
· 有大规模数据质量评估的经验
加入我们
· 独立负责产品核心差异化功能
· 参与全球使用的模型开发，而不仅仅是在英语市场
· 与一支高素质的小团队合作，拥有深厚的机器学习和系统经验
· 具有竞争力的薪酬 + A轮阶段有意义的股权
最初发布于 Himalayas
查看英文原文
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