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Open nowPosted 239 days ago

软件工程师 - 大模型端侧优化和部署

ponyai/ponyai76 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now软件工程师 - 大模型端侧优化和部署ponyai/ponyai · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. ponyai/ponyai postings stay open a median of 4 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: posted 239 days ago

ponyai/ponyai median: 4 days open

The posting

负责将大模型部署到自动驾驶车端芯片平台（NVIDIA Orin / Thor 等），并进行极致的性能、内存与能效优化。你将在算力、内存、功耗与实时性严格受限、且对安全与稳定性要求极高的车载环境下，打通从推理引擎定制、核心算子优化到车端运行时管理的全栈链路，推动大模型在自动驾驶（端到端决策、场景理解等）场景的规模化落地。

岗位职责 1. 适配、移植与深度优化车端推理引擎/运行时（TensorRT-LLM、TensorRT Edge-LLM、vLLM、SGLang 等），通过计算图优化、算子融合、高性能 CUDA 算子开发及 KV Cache、投机采样等优化，充分发挥 Orin / Thor 平台算力，满足实时性要求； 2. 建立 Benchmark 与 Profiling 体系，跟踪首字延迟（TTFT）、吞吐、内存峰值、冷启动、模型切换、cache 命中率、长稳与抖动等车端核心指标。支持场景理解、Function Calling、结构化输出、流式输出、多模态推理等核心场景的低延迟落地；

1. 计算机、电子工程、人工智能、自动化等相关专业，本科及以上学历； 2. 具备 2 年以上 AI 推理部署、端侧AI、系统性能优化或模型工程化相关经验； 3. 精通 C/C++ 与 Python，具备扎实的计算机体系结构知识，熟悉 Linux 下的开发与调试； 4. 深入理解 Transformer / LLM / VLM 推理机制，熟悉 prefill、decode、KV Cache、attention、batching、streaming 等关键概念； 5. 精通至少一种推理框架（TensorRT-LLM / vLLM / SGLang / ONNX Runtime 等），具备引擎定制、算子开发或 graph 优化经验； 6. 精通大模型核心算子（Attention、FFN/MoE、LayerNorm/RMSNorm）的底层实现与性能优化方法； 加分项 1. 有 Qwen 等主流开源大模型的部署经验； 2. 有在 NVIDIA Orin / Thor 等车端平台部署大模型的经验（熟悉 TensorRT Edge-LLM 等工具链，了解 Orin 支持 FP16/INT8/INT4、Thor 支持 FP8/NVFP4 等平台特性差异）； 3. 有自动驾驶大模型量产落地经验，有端到端自动驾驶大模型或多模态大模型部署优化经验。

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