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Open nowPosted 1397 days ago

大模型研究实习岗位

houmo/campus8 open roles

Where
Nanjing, Jiangsu, Chinese Mainland
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Your applicationOpen now大模型研究实习岗位houmo/campus · Nanjing, Jiangsu, Chinese Mainland

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13.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 4.6%3 days
  3. 13.3%7 days
  4. 21.3%14 days
  5. 35.7%30 days
  6. 59.1%60 days
This job: posted 1397 days ago

The posting

研究方向：探索大模型效率突破的核心技术 围绕大模型推理性能瓶颈，开展前沿算法研究与落地验证，具体方向包括： 1. 大模型压缩加速算法研究：针对大模型 “大参数、高算力” 痛点，深入研究 Scalar/Vector 量化、自动混合精度、KVCache 压缩、MXFP8 新型数据格式等技术，探索低精度场景下 “精度无损 / 微损” 与 “性能最大化” 的平衡方案，推动算法在实际业务中落地。 2. AI 前沿技术跟踪与落地：密切关注国际AI顶会及工业界大模型推理领域的最新研究成果，提炼可落地的技术方向，参与优化自研 NPU 的软硬件协同加速方案，打通 “算法理论 - 硬件实现” 链路。

开发方向：构建大模型高效落地的工程底座 依托自研硬件与业务场景，开发支撑大模型全生命周期的 AI Infra 工具与模块，具体方向包括： 1. AI Infra 工具链开发：围绕大模型 “部署 - 推理 - 运维” 全流程效率提升，负责模型解析、图优化、算子加速等核心模块开发，确保大模型在自研 NPU 上的部署流程自动化、高性能、高可靠。 2. 大模型 Infra 加速与算子优化：聚焦大模型核心计算模块效率提升，研究 MoE 专家负载均衡、Attention 性能优化、非线性算子加速等技术，优化LLMs/VLMs/MLLMs边端推理效率（如算力、功耗、latency、bandwidth等）。 3. 大模型部署评测与优化：负责大模型在自研 NPU 上的端到端部署，搭建精度验证、性能测试、Benchmark 对比体系，定位并解决部署中的精度漂移、性能瓶颈问题，保障模型落地效果。

携手未来：为你的成长提供超预期支持 1. 竞争力薪酬回报：表现优异者，实习薪资可对标社招同级别水平，你的技术实力将获得即时、对等的现金认可，拒绝 “廉价实习”。 2. 优先转正机会：实习表现优秀者可直接获得正式岗位录用资格，跳过校招繁琐筛选流程，无缝衔接从 “学生” 到 “行业从业者” 的身份转变。 3. 一流学府深造推荐：表现突出者可获推荐至北大、复旦、南大、上交、东南等国内一流高校和欧美名校合作导师团队继续深造（如研究生、博士生、博士后）。 4. 核心项目参与权：直面工业界真实挑战（如千万级用户依赖的AI产品），你的代码与方案将直接影响产品体验，亲身参与技术价值创造。 5. 1v1 资深导师指导：配备资深研究员（AI顶会作者）1v1指导，从 “技术方向选择” 到 “工程问题 debug” 提供全流程指导，避免成长走弯路。 6. 国际顶会参与机会：表现突出者可获公司全额资助（差旅 + 注册费）参加国际权威 AI 会议（包括但不限于 CVPR、NeurIPS、ICML 等），与全球优秀学者交流，积累学术视野。

团队部分研究成果 1. GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training (ACL-2025) 2. MambaQuant: Quantizing the Mamba Family With Variance Aligned Rotation (ICLR-2025) 3. OSTQuant: Refining LLM Quantization with Orthogonal And Scaling Transforms (ICLR-2025) 4. MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance (ICML-2025) 5. RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization. (ICML-2025) 6. EA-ViT: Efficient Adaptation for Elastic Vision Transformer (ICCV-2025) 7. BBAL: A Bidirectional Block Floating Point-Based Quantisation Accelerator for Large Language Models (DAC-2025) 8. NVR: Vector Runahead on NPUs for Sparse Memory Access (DAC-2025) 9. Pushing the Limits of BFP on Narrow Precision LLM Inference (AAAI-2025) 10. MQuant: Unleashing the inference potential of multimodal large language models via full static quantization (ACMMM-2025) 11. Pillarhist: A quantization-aware pillar feature encoder based on height-aware histogram (CVPR-2025) 12. A 22nm 64kb Lightning-like Hybrid Computing-in-Memory Macro with Compressor-based Adder-tree and Analog-storage Quantizer for Transformer and CNNs (ISSCC-2024) 13. MIM4DD: Mutual Information Maximization for Dataset Distillation (NeuIPS-2023) 14. Post-training Quantization on Diffusion Models (CVPR-2023) 15. PD-Quant: Post-Training Quantization based on Prediction Difference Metric (CVPR-2023) 16. 3DPEE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers (ICCV 2023) 17. PTQ4ViT: Post-Training Quantization Framework for Vision Transformers (ECCV-2022)

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