高级机器学习工程师 | 德国 (3 个月项目)
Senior ML Engineer | Germany (3 Month project)
我们正在寻找一位经验丰富的机器学习工程师/MLOps工程师，加入一个面向德国客户的云原生项目。
该职位以工程为主，涉及构建生产级的机器学习基础设施，使用GPU工作负载、机器学习流水线、大语言模型和大规模数据处理。
📍 地点：德国
🗣 德语：B2+ - 必须掌握
🗣 英语：B1+
📅 预计开始时间：2026年9月30日
你将参与的工作
- 使用Kubeflow Pipelines（KFP v2）构建和编排机器学习流水线
- 在GPU上训练机器学习模型，并在Kubernetes中管理GPU资源
- 对transformers和大语言模型进行微调
- 使用MLflow跟踪实验和模型
- 使用XGBoost和CatBoost构建传统机器学习模型
- 使用SQL Server和DuckDB处理大型数据集
- 开发基于Python的流水线、集成和工具
- 通过测试、干净代码和GitLab CI的CI/CD保持高标准的工程实践
- 在安全、零信任/默认安全的环境中工作，包括网络策略和限制性容器权限
要求
我们寻找的人
- 有Kubeflow Pipelines的经验，最好是KFP v2
- 有在GPU上训练模型的经验
- 有LLM/transformer微调的实际经验
- 有MLflow的经验
- 精通XGBoost、CatBoost或类似的提升模型
- 强大的Python工程技能
- 扎实的SQL经验，理解大规模数据处理
- 有CI/CD、干净代码和自动化测试的经验
- 有超越笔记本实验的生产级机器学习/MLOps经验
- 有在企业或受监管的云原生环境工作的经验
加分项
- 有LLM预训练经验，而不仅仅是微调
- Kubernetes中的GPU编排经验
- 有零信任环境、网络策略和限制性容器权限的经验
- 了解DuckDB
- 有现代Python工具如uv的经验
之前不需要有医疗或账单领域的经验，但你需要能够快速适应新领域。
查看英文原文
We are looking for an experienced We are looking for an experienced ML Engineer / MLOps Engineer to join a cloud-native project for a German customer.
The role is strongly engineering-focused and involves building production-grade ML infrastructure, working with GPU workloads, ML pipelines, LLMs and large-scale data processing.
📍 Location: Germany
🗣 German: B2+ - must-have
🗣 English: B1+
📅 Estimated start: September 30, 2026
What you'll be working on
- Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2)
- Train ML models on GPUs and manage GPU resources within Kubernetes
- Fine-tune transformers and LLMs
- Track experiments and models using MLflow
- Build classical ML models with XGBoost and CatBoost
- Process large datasets using SQL Server and DuckDB
- Develop Python-based pipelines, integrations and tooling
- Maintain high engineering standards through testing, clean code and CI/CD with GitLab CI
- Work in a secure, zero-trust / secure-by-default environment with network policies and restrictive container permissions
Requirements
What we're looking for
- Hands-on experience with Kubeflow Pipelines, ideally KFP v2
- Experience training models on GPUs
- Practical experience with LLM / transformer fine-tuning
- Experience with MLflow
- Strong knowledge of XGBoost, CatBoost or similar boosting models
- Strong Python engineering skills
- Solid SQL experience and understanding of large-scale data processing
- Experience with CI/CD, clean code and automated testing
- Production-grade ML/MLOps experience beyond notebook-based experimentation
- Experience working in enterprise or regulated cloud-native environments
Nice to have
- Experience with LLM pre-training, beyond fine-tuning
- GPU orchestration in Kubernetes
- Experience with zero-trust environments, network policies and restrictive container rights
- Knowledge of DuckDB
- Experience with modern Python tooling such as uv
Previous healthcare or billing domain experience is not required, but you should be comfortable quickly getting up to speed with a new domain.
Originally posted on Himalayas