AI研究员 — 蒸馏
AI Researcher — Distillation
职位描述
我们正在寻找一名专注于模型压缩的AI研究员，帮助我们推动高效、高性能模型的前沿发展。你将致力于将大型、昂贵的模型转化为更小、更快、更易部署的系统，同时保持或提升性能。
这个职位适合喜欢发表研究、贴近实际系统工作，并看到自己的想法从论文→代码→生产环境的人。
你将参与的工作
- 设计和评估模型压缩技术（教师-学生训练、自压缩、逐层压缩、表征匹配等）
- 研究模型大小、延迟、内存和准确率之间的权衡
- 开发针对以下场景的新压缩方法：
- 大型语言模型
- 长上下文或专用架构
- 推理受限的环境
- 运行大规模实验和消融实验；严谨分析结果
- 与工程师合作，将研究成果产品化
- 撰写并提交研究论文至顶级会议（NeurIPS、ICML、ICLR、COLM等）
- 在适当的时候，为内部研究笔记、技术博客和开源项目做贡献
我们寻找的人选
要求
- 扎实的机器学习研究背景
- 具有模型压缩或相关主题（压缩、剪枝、量化、表征学习）的实际经验
- 发表过研究论文的经历（会议或期刊论文、研讨会论文或arXiv预印本）
- 对深度学习基础有扎实理解（优化、训练动态、泛化能力）
- 熟练使用PyTorch（或其他类似框架）并具备研究级实验能力
- 能清晰地表达研究思路、结果和局限性
加分项
- 具有压缩大型语言模型的经验
- 从事过以效率为导向的研究（延迟、内存、吞吐量）
- 具有长上下文模型或非Transformer架构的经验
- 在机器学习或研究工具方面有开源贡献
- 有初创公司或应用研究经验
加入我们的理由
- 在A轮融资阶段对研究方向拥有真正的主导权
- 强有力的出版和开放研究支持
- 研究与真实世界部署之间紧密的反馈循环
- 可以接触到有意义的计算资源和生产规模的问题
- 小而技术实力强的团队，具备深厚的机器学习和系统专业知识
示例背景
- 从学术界转向工业界的机器学习研究员
- 在模型效率领域有发表成果的研究工程师
查看英文原文
About the Role
We’re looking for an AI Researcher focused on model distillation to help us push the frontier of efficient, high-performance models. You’ll work on turning large, expensive models into smaller, faster, and more deployable systems—while maintaining or improving quality.
This role is ideal for someone who enjoys publishing research, working close to real systems, and seeing their ideas move from papers → code → production.
What You’ll Work On
- Design and evaluate model distillation techniques (teacher–student training, self-distillation, layer-wise distillation, representation matching, etc.)
- Research tradeoffs between model size, latency, memory, and accuracy
- Develop novel distillation approaches for:
- Large language models
- Long-context or specialized architectures
- Inference-constrained environments
- Run large-scale experiments and ablations; analyze results rigorously
- Collaborate with engineers to productionize research outcomes
- Write and submit research papers to top-tier venues (NeurIPS, ICML, ICLR, COLM, etc.)
- Contribute to internal research notes, technical blogs, and open-source projects when appropriate
What We’re Looking For
Required
- Strong background in machine learning research
- Hands-on experience with model distillation or closely related topics (compression, pruning, quantization, representation learning)
- Publication experience (conference or journal papers, workshop papers, or arXiv preprints)
- Solid understanding of deep learning fundamentals (optimization, training dynamics, generalization)
- Fluency in PyTorch (or equivalent) and research-grade experimentation
- Ability to clearly communicate research ideas, results, and limitations
Nice to Have
- Experience distilling large language models
- Work on efficiency-focused research (latency, memory, throughput)
- Experience with long-context models or non-Transformer architectures
- Open-source contributions in ML or research tooling
- Prior startup or applied research experience
Why Join Us
- Real ownership over research direction at a Series A stage
- Strong support for publishing and open research
- Tight feedback loop between research and real-world deployment
- Access to meaningful compute and production-scale problems
- Small, highly technical team with deep ML and systems expertise
Example Backgrounds
- ML researchers from academia transitioning to industry
- Research engineers with published work in model efficiency
- PhD / Post-doc graduates or industry researchers who still want to publish
Originally posted on Himalayas