AI大模型算法专家LLM Algorithm Expert

职位描述

.主导大模型的算法研究、训练调优及工程化落地，提升模型性能和效率；

.主导大模型压缩、分布式训练、推理加速等技术落地（如量化、MoE、FlashAttention等）；

.结合业务场景（如对话系统、内容生成、知识推理）设计模型优化方案，解决数据稀疏性、幻觉抑制等挑战；

.跟踪学术界与工业界最新进展，推动技术成果转化；

.主导技术方案输出，协同工程团队实现高性能服务部署；

.参与Agent架构设计，完成整体Agent项目落地。

任职资格

.硕士及以上学历，2年以上大模型研发经验，5年以上算法研发经验；

.精通PyTorch/TensorFlow框架，熟悉分布式训练工具；

.深入掌握Transformer架构及衍生技术，有丰富的大模型调优经验；

.熟练掌握大模型微调、蒸馏、强化学习等训练技术；

.具备完整的大模型训练-部署全链路经验；

.熟练掌握Agent架构；

.有顶会论文发表者，英语流利优先。

Job Description

• LLM R&D & Deployment: Lead algorithm research, training, fine-tuning, and engineering implementation of Large Language Models (LLMs) to enhance model performance and efficiency.
• Optimization & Acceleration: Drive the implementation of LLM compression, distributed training, and inference acceleration techniques (e.g., quantization, MoE, FlashAttention).
• Domain Adaptation & Challenge Mitigation: Design model optimization strategies tailored to business scenarios (e.g., conversational AI, content generation, knowledge reasoning) to address challenges like data sparsity and hallucination mitigation.
• Tech Transfer: Track frontier developments across academia and industry to drive the commercialization of technical achievements.
• Architecture & High-Performance Deployment: Lead the output of technical architecture solutions, collaborating with engineering teams to deploy high-performance model services.
• Agent Architecture: Participate in Agent system architecture design and drive end-to-end Agent project execution.

Qualifications

• Education & Experience: Master's degree or above; 2+ years of LLM R&D experience, with 5+ years of overall algorithm research and development experience.
• Frameworks & Infrastructure: Proficient in PyTorch/TensorFlow frameworks and experienced with distributed training tools.
• Core Architecture & Fine-Tuning: Deep mastery of Transformer architecture and related variant technologies, backed by extensive experience in LLM tuning.
• Training Techniques: Proficient in LLM fine-tuning, knowledge distillation, and reinforcement learning techniques.
• Full-Lifecycle Experience: Proven track record spanning the entire end-to-end pipeline from LLM training to deployment.
• Agent Expertise: Fluent in AI Agent architecture principles and design.
• Publications (Preferred): First-author or major publications in top-tier conferences are preferred.