大模型LLM算法实习生- 模型加速 (Jul - Dec 2025)
AMD
On-site π’ 11 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 applicantsβ needs under the respective laws throughout all stages of the recruitment and selection process.Responsibilities
οΌε¦ε·²ε¨BOSSη΄θζιοΌθ―·εΏε¨ζ€ιε€ζιοΌ Responsibilities: Participate in research and implementation of inference optimization for large language models (LLMs) or multimodal foundation models; Investigate and apply state-of-the-art model compression and optimization techniques, including quantization, pruning, KV cache optimization, operator fusion, tensor parallelism, etc.; Analyze and optimize existing inference frameworks (e.g., TensorRT, FasterTransformer, vLLM, DeepSpeed), and contribute to system-level performance improvements; Support the team in technical validation, benchmarking, and documentation of research outcomes. Requirements: Masterβs or PhD student in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field; Solid understanding of deep learning fundamentals and Transformer architectures; Proficient in at least one mainstream deep learning framework (e.g., PyTorch or TensorFlow), with strong coding skills; Publications in top-tier AI conferences (e.g., NeurIPS, ICLR, CVPR, ACL, AAAI) are highly preferred; Prior experience in one or more of the following is a strong plus: Large model inference acceleration (e.g., quantization, compiler-based optimization, distributed inference) Model compression techniques (e.g., distillation, structured pruning, sparsity) High-performance computing (e.g., CUDA programming, kernel-level tensor optimizations) Bonus Points: Familiarity with mainstream LLMs (e.g., LLaMA, GPT, Mistral, DeepSeek) Open-source contributions to projects such as HuggingFace, vLLM, OpenXLA, etc. Experience in hardware-aware AI optimization (e.g., NPU/GPU backend tuning, compiler stack development) #LI-EJ1 #LI-HYBRIDSimilar jobs
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