实习 - 机器学习研究工程师
Internship - Machine Learning Research Engineer
Internship Program Berlin
Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office.
Responsibilities
- 不断推动搜索质量提升——通过模型、数据、工具或其他可用手段。
- 使用 PyTorch 等框架训练并优化大规模深度学习模型，利用分布式训练（例如 PyTorch Distributed、DeepSpeed、FSDP）和硬件加速，重点关注检索和排序模型。
- 在表示学习领域进行研究，包括对比学习、多语言、评估以及用于搜索和检索的多模态建模。
- 构建并优化 RAG 流水线以实现知识定位和答案生成。
Qualifications
- 理解搜索和检索系统，包括质量评估原则和指标。
- 熟练掌握 PyTorch，具备分布式训练技术和大型模型性能优化经验。
- 对表示学习感兴趣，包括对比学习、密集与稀疏向量表示、表示融合、跨语言表示对齐、训练数据优化和稳健评估。
- 在 AI/ML 会议或工作坊中有发表论文记录（例如 NeurIPS、ICML、ICLR、ACL、EMNLP、SIGIR）。
查看英文原文
Internship Program Berlin
Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office.
Responsibilities
- Relentlessly push search quality forward — through models, data, tools, or any other leverage available.
- Train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models.
- Conduct research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval.
- Build and optimize RAG pipelines for grounding and answer generation.
Qualifications
- Understanding of search and retrieval systems, including quality evaluation principles and metrics.
- Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.
- Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation.
- Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).