大模型LLM算法实习生 - 模型量化/低比特训练推理 (Jul - Dec 2025)
AMD
On-site 🏢 17 April
Data Science & AI/ML
Beijing, China 🇨🇳
Qualifications
Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.Responsibilities
An exciting internship opportunity to make an immediate contribution to AMD's next generation of technology innovations awaits you! We have a multifaceted, high-energy work environment filled with a diverse group of employees, and we provide outstanding opportunities for developing your career. During your internship, our programs provide the opportunity to collaborate with AMD leaders, receive one-on-one mentorship, attend amazing networking events, and much more. Being part of AMD means receiving hands-on experience that will give you a competitive edge. Together We Advance your career! JOB DETAILS: Location: Beijing, China Onsite/Hybrid: Onsite or remote Duration: Jul. 2025 to Dec. 2025 WHAT YOU WILL BE DOING: We are seeking highly motivated AI Algorithm Engineering intern/co-op to join our team. In this role – 1. 研究和开发大模型优化算法，包括但不限于模型量化、模型剪枝、模型蒸馏、模型压缩等，提高训练和推理性能； 2. 重点关注低比特数据类型的模型训练，分析和解决精度与性能问题； 3. 针对应用场景和硬件资源（如CPU/GPU/NPU）进行模型优化和部署。 WHO WE ARE LOOKING FOR: 1. 深入理解大模型算法原理，至少熟悉一种生成模型；或者有模型量化/剪枝/蒸馏/压缩相关科研或项目经验； 2. 了解Flash Attention/Memory Efficient Attention等kernel的实现方法，具备kernel开发和优化能力； 3. 熟悉Python/C/C++编程，熟练掌握PyTorch等至少一种深度学习框架； 加分项： 5. 在顶级AI会议或期刊上发表过论文，或参加过国际竞赛并取得优异成绩者优先； 6. 熟悉Triton/Cutlass者优先； 7. 有大模型分布式训练经验者优先。 #LI-EJ1 #LI-HYBRIDSimilar jobs
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