AI算法工程师（物联） AI Algorithm Engineer

1、参与业务AI需求调研与拆解，在导师指导下输出技术方案，协助完成AI建模、算法优化与功能开发。

2、参与大模型微调与部署的全流程学习，包括模型选型、硬件适配、参数优化及生产环境部署。

3、协助完成业务数据清洗、预处理及数据集搭建，参与模型开发、版本管理及部署上线的标准化流程。

4、跟进前沿AI技术与开源模型动态，参与技术调研与选型验证，协助优化现有AI系统与模型效果

1、2027届全日制硕士及以上学历，计算机、人工智能、软件工程、数据科学等相关专业优先。

2、熟悉Python及PyTorch/TensorFlow等主流AI框架，有课程项目或竞赛实践经验者优先。

3、熟悉Linux开发环境，了解后端开发、数据清洗与特征工程基本方法，有模型工程化或部署实践经验优先。

4、具备快速学习与主动钻研意识，善于沟通协作，执行力强，乐于在团队中承担开发任务并主动推进。

AI Algorithm Engineer

Job Responsibilities

1. Participate in the research and decomposition of business AI requirements, formulate technical solutions under mentor guidance, and assist in AI modeling, algorithm optimization and function development.
2. Learn the full lifecycle of large model fine-tuning and deployment, including model selection, hardware adaptation, parameter optimization and deployment in production environments.
3. Assist in business data cleansing, preprocessing and dataset construction, and take part in standardized workflows covering model development, version control, deployment and release.
4. Keep track of cutting-edge AI technologies and open-source model updates, engage in technical research, selection and verification, and help improve the performance of existing AI systems and models.

Job Requirements

1. Full-time Master’s degree or above, expected graduation in 2027. Majors in Computer Science, Artificial Intelligence, Software Engineering, Data Science or related fields are preferred.
2. Proficient in Python and mainstream AI frameworks such as PyTorch / TensorFlow. Candidates with coursework projects or competition experience are preferred.
3. Familiar with Linux development environment; understand basic methods of back-end development, data cleansing and feature engineering. Practical experience in model engineering or deployment is a plus.
4. Strong self-motivation for rapid learning and independent research, good communication and teamwork skills, excellent execution, and willingness to undertake development tasks and drive progress proactively within the team.

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。

 2.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，解决特定场景样本稀缺问题。

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。

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：

 1.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）。

 2.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。

 3.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：

 1.有多模态大模型（VLM）数据标注或微调经验者优先。

 2.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

1. Model Training & Optimization: Conduct training, tuning and quantization of end-side object detection/segmentation models, and optimize inference performance under resource-constrained environments.
2. 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.
3. 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

1. Education Background: Postgraduate students majoring in Computer Science, Automation, Electronic Information and related disciplines; outstanding undergraduates are also acceptable.

Core Skills

1. Solid foundation in deep learning, proficient in PyTorch framework, familiar with mainstream object detection algorithms (YOLO series, RT-DETR, etc.).
2. Familiar with common image AIGC tools and technologies, able to write scripts for batch data generation and quality filtering.
3. Practical deployment experience on RKNN platform, understanding the principles of model quantization (INT8/FP16) and its impact on model accuracy (not mandatory).

Preferred Qualifications

1. Experience in data labeling or fine-tuning for Vision-Language Multimodal Large Models (VLM).
2. Basic knowledge of C/C++, familiar with cross-compilation on Linux/Android (Android prioritized) and multi-channel video stream processing on edge devices.