AI 研究员 — AI 架构研究
AI Researcher — AI Architecture Research
职位描述
我们正在寻找一位专注于AI架构研究的AI研究员，帮助设计、分析并推进下一代模型架构。你将在理论与生产系统的交汇处工作——发表创新研究成果，同时与工程师紧密合作，将想法转化为实际系统。
这个职位适合有研究论文发表经历的人，希望自己的工作能直接影响部署的模型，而不仅仅是基准测试。
你将参与的工作
- 研究和设计新型AI架构（例如标准Transformer架构的替代方案、长上下文模型、高效序列建模、混合架构）
- 探索可扩展性、效率和稳定性的架构改进
- 通过消融实验、基准测试和实证研究进行原型设计和评估
- 为顶级机器学习会议和期刊撰写和合著研究论文
- 与工程团队合作，将研究成果转化为训练和推理系统
- 跟踪最前沿的研究，尽早识别有前景的方向
我们寻找的人选
- 在机器学习研究方面有扎实背景，重点在模型架构
- 在ML/AI领域有论文发表记录（例如NeurIPS、ICML、ICLR、COLM、ACL、EMNLP、arXiv）
- 深入理解：
- 神经网络架构
- 序列模型和注意力机制
- 训练动态和优化
- 具备PyTorch或JAX的实战经验
- 能够严谨推理、设计清晰的实验并清晰地传达结果
- 适应快节奏的创业环境
加分项
- 具有非Transformer架构的经验（例如基于RNN、状态空间、混合模型）
- 从事长上下文或内存高效的模型工作
- 开源研究贡献
- 具有连接研究与生产系统的经验
- 具有高效训练或推理感知架构设计的背景
加入我们的理由
- 对研究方向和路线图有高度自主权
- 明确的路径去发表有影响力的工作
- 研究与实际部署之间的紧密反馈循环
- 小型、高度技术化的团队，具有强大的研究文化
- 竞争性的薪酬和有意义的股权
最初发布于喜马拉雅斯
查看英文原文
About the Role
We’re looking for an AI Researcher focused on AI architecture research to help design, analyze, and advance next-generation model architectures. You’ll work at the intersection of theory and production—publishing novel research while collaborating closely with engineers to turn ideas into real systems.
This role is ideal for someone who has published research papers and wants to see their work directly shape deployed models, not just benchmarks.
What You’ll Work On
- Research and design novel AI architectures (e.g. alternatives to standard Transformer designs, long-context models, efficient sequence modeling, hybrid architectures)
- Explore architectural improvements for scalability, efficiency, and stability
- Prototype and evaluate new architectures through ablations, benchmarks, and empirical studies
- Author and co-author research papers for top ML conferences and journals
- Collaborate with engineering teams to translate research into training and inference systems
- Stay current with state-of-the-art research and identify promising directions early
What We’re Looking For
- Strong background in machine learning research, with a focus on model architecture
- Publication record in ML/AI venues (e.g. NeurIPS, ICML, ICLR, COLM, ACL, EMNLP, arXiv)
- Deep understanding of:
- Neural network architectures
- Sequence models and attention mechanisms
- Training dynamics and optimization
- Hands-on experience with PyTorch or JAX
- Ability to reason rigorously, design clean experiments, and communicate results clearly
- Comfortable working in a fast-moving startup environment
Nice to Have
- Experience with non-Transformer architectures (e.g. RNN-based, state-space, hybrid models)
- Work on long-context or memory-efficient models
- Open-source research contributions
- Experience bridging research and production systems
- Background in efficient training or inference-aware architecture design
Why Join Us
- High ownership over research direction and roadmap
- Clear path to publishing impactful work
- Tight feedback loop between research and real-world deployment
- Small, highly technical team with strong research culture
- Competitive compensation and meaningful equity
Originally posted on Himalayas