AI算法工程师 AI Algorithm Engineer (MJ000529)

该职位来源于猎聘 岗位职责： 1.模型训练与优化：负责端侧目标检测/分割模型的训练、调优与量化，优化模型在资源受限环境下的推理性能。

• AIGC数据增广：利用生成式AI技术（如 Diffusion 模型、ControlNet、GANs 等）进行训练样本的数据生成与图像增广，解决特定场景样本缺少问题。 3.端侧芯片部署：负责算法在 Rockchip（如 RK3568/RK3588/RK3566 等） 平台的移植与部署，熟练完成模型转换（如 ONNX 转 RKNN）及 NPU 加速调试。 理者优先。 岗位要求： 1.教育背景：计算机、自动化、电子信息等相关专业硕士在读（或优秀的本科生及以上）。 核心技能：
• 扎实的深度学习基础，熟练掌握 PyTorch 框架，熟悉常用目标检测算法（如 YOLO 系列、RT-DETR 等）。
• 熟悉常见的图像 AIGC 工具与技术，能够编写脚本进行批量数据生成与质量筛选。
• 具备 RKNN 平台实际部署经验，理解模型量化（INT8/FP16）原理及其对精度的影响。（非必须） 加分项：
• 有多模态大模型（VLM）数据标注或微调经验者优先。
• 具备 C/C++ 基础，了解 Linux/Android（主要是Android） 端侧交叉编译及边缘端多路视频流处 Job Responsibilities Model Training & Optimization: Conduct training, tuning and quantization of end-side object detection/segmentation models, and optimize inference performance under resource-constrained environments. AIGC Data Augmentation: Leverage generative AI technologies (Diffusion, ControlNet, GANs, etc.) to generate training samples and implement image augmentation, so as to address the scarcity of samples in specific scenarios. End-side Chip Deployment: Complete algorithm transplantation and deployment on Rockchip platforms (RK3568/RK3588/RK3566, etc.), perform model conversion (ONNX to RKNN) and debugging for NPU acceleration. Job Requirements Education Background: Postgraduate students majoring in Computer Science, Automation, Electronic Information and related disciplines; outstanding undergraduates are also acceptable. Core Skills Solid foundation in deep learning, proficient in PyTorch framework, familiar with mainstream object detection algorithms (YOLO series, RT-DETR, etc.). Familiar with common image AIGC tools and technologies, able to write scripts for batch data generation and quality filtering. Practical deployment experience on RKNN platform, understanding the principles of model quantization (INT8/FP16) and its impact on model accuracy (not mandatory). Preferred Qualifications Experience in data labeling or fine-tuning for Vision-Language Multimodal Large Models (VLM). Basic knowledge of C/C++, familiar with cross-compilation on Linux/Android (Android prioritized) and multi-channel video stream processing on edge devices.