人工智能研究员——训练优化
AI Researcher — Training Optimization
职位描述
我们正在寻找一名专注于训练优化的AI研究员，帮助我们提升大规模模型训练的效率、稳定性和可扩展性。你将在研究与系统之间工作，开发新技术以降低训练成本、加速收敛并提高模型质量——同时通过严谨的实验和发表成果来验证这些想法。
这个职位适合那些喜欢将研究洞察转化为实际训练成果的人，且有发表应用机器学习研究成果的经验（或强烈意愿）。
你将参与的工作
- 为大型模型设计和评估训练优化技术（例如优化算法、调度器、归一化、课程策略）
- 提升长周期和大数据集上的训练效率和稳定性
- 研究并实现以下方法：
- 优化器和调度器创新
- 混合精度、低精度和内存高效训练
- 梯度噪声减少、缩放定律和收敛分析
- 训练时正则化和鲁棒性技术
- 运行大规模实验，分析结果，并将发现转化为可操作的改进
- 撰写或共同撰写研究论文、技术报告或博客文章
- 与基础设施和推理团队紧密合作，确保训练决策能转化为实际性能
我们期望的条件
- 扎实的机器学习研究背景，重点在训练动态和优化方面
- 有训练大型神经网络的经验（如LLM、多模态模型或大型序列模型）
- 在机器学习领域发表过论文（如NeurIPS、ICML、ICLR、ACL、EMNLP、COLM、arXiv）或有同等质量的开源研究成果
- 对以下内容有扎实的理解：
- 优化理论与实践
- 反向传播、梯度流动和训练稳定性
- 分布式和大批次训练
- 熟练使用Python和现代机器学习框架（PyTorch优先）
- 能够独立设计实验并从数据中进行推理
加分项
- 非标准架构经验（如RNN变体、长上下文模型、混合系统）
- 在大规模GPU上优化训练的经验（如FSDP、ZeRO、自定义内核）
- 对开源机器学习或研究代码库有贡献
- 适应快速变化、不确定的创业环境
为什么选择这个职位
- 对核心模型训练决策有实际影响力
- 有自由去探索和发表创新成果
查看英文原文
About the Role
We’re looking for an AI Researcher focused on training optimization to help us push the efficiency, stability, and scalability of large-scale model training. You’ll work at the intersection of research and systems, developing novel techniques to reduce training cost, accelerate convergence, and improve model quality—while validating ideas through rigorous experiments and publications.
This role is ideal for someone who enjoys turning research insights into practical training wins, and who has a track record (or strong ambition) of publishing applied ML research.
What You’ll Work On
- Design and evaluate training optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies)
- Improve training efficiency and stability across long runs and large datasets
- Research and implement methods such as:
- Optimizer and scheduler innovations
- Mixed-precision, low-precision, and memory-efficient training
- Gradient noise reduction, scaling laws, and convergence analysis
- Training-time regularization and robustness techniques
- Run large-scale experiments, analyze results, and translate findings into actionable improvements
- Author or co-author research papers, technical reports, or blog posts
- Collaborate closely with infrastructure and inference teams to ensure training decisions translate to real-world performance
What We’re Looking For
- Strong background in machine learning research, with emphasis on training dynamics and optimization
- Experience training large neural networks (LLMs, multimodal models, or large sequence models)
- Publication experience in ML venues (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, arXiv) or equivalent high-quality open research
- Solid understanding of:
- Optimization theory and practice
- Backpropagation, gradient flow, and training stability
- Distributed and large-batch training
- Proficiency in Python and modern ML frameworks (PyTorch preferred)
- Ability to independently design experiments and reason from data
Nice to Have
- Experience with non-standard architectures (e.g. RNN variants, long-context models, hybrid systems)
- Experience optimizing training on GPUs at scale (FSDP, ZeRO, custom kernels)
- Contributions to open-source ML or research codebases
- Comfort operating in fast-moving, ambiguous startup environments
Why This Role
- Real influence over core model training decisions
- Freedom to pursue and publish novel research
- Direct access to large-scale experiments and real production constraints
- A small, senior team that values thinking deeply and shipping thoughtfully
Originally posted on Himalayas