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Open nowPosted 75 days ago

优才-低精度量化/模型压缩算法工程师

agirobot/94699358 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now优才-低精度量化/模型压缩算法工程师agirobot/946993 · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 75 days ago

The posting

1. 负责大模型、多模态模型、具身智能模型的低精度量化、模型压缩、蒸馏、剪枝、稀疏化算法研发与落地。 2. 研究并实现 INT4/NVFP4/INT8/FP8 等量化方案：GPTQ、AWQ、SmoothQuant、GPTQ-For-LLaMA、bitsandbytes 等。 3. 负责模型压缩算法在训练、推理、端边、云端不同场景的精度保持与性能收益。 4. 与推理引擎、算子团队协同，将量化 / 压缩算法对接 TensorRT-LLM、vLLM、ONNX Runtime、NPU/GPU等硬件与推理栈。 5. 构建自动化量化工具链、精度评估体系、压缩后模型稳定性验证流程。 6. 针对机器人端侧（Jetson//Thor/Orin/NPU）等资源受限场景，实现极小内存、极低功耗下的模型部署。 7. 输出算法方案、实验报告、性能 / 精度对比，支撑业务规模化降本增效。

1. 硕士及以上，计算机/人工智能/电子/数学相关，2年以上模型量化或压缩经验。 2. 精通深度学习模型结构：Transformer、Attention、FFN、Norm、激活函数。 3. 熟悉主流量化算法：PTQ、QAT、GPTQ、AWQ、SmoothQuant、LLM.int8 ()、FP8 量化。 4. 熟悉模型压缩技术：剪枝、知识蒸馏、动态稀疏、低秩分解（LoRA/QLoRA）。 5. 熟练使用 PyTorch，具备模型训练、微调、精度调试能力。 6. 了解 GPU/NPU 底层特性，熟悉量化算子、Kernel 对齐、推理引擎流程优先。 7. 有LLM/VLM/具身智能模型量化落地经验者优先。 8. 具备良好的算法推导、实验设计、问题定位能力。

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