携程集团(Trip.com Group)
AI大模型算法专家LLM Algorithm Expert
岗位职责
.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
任职要求
.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.
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