【字节算法内推】字节跳动tiktok商业化算法实习/社招内推
字节跳动tiktok商业化招聘日常实习生、算法工程师，背景为CV、NLP、多模态、图网络、音频均可，base上海or新加坡，简历可投递至： zhanghantao.022@bytedance.com，直推组内，可跟进面试进度。社招参考JD如下：
「机器学习算法工程师」（上海）
职位描述
1. 负责广告、电商、直播等相关内容理解，包括图片、文本、视频、音频等； 2. 负责数据挖掘与特征工程，构建机器学习模型，构建商业化生态； 3. 面对千万量级业务/受限资源(例如端上)等场景，优化模型计算效率，提升模型可用性； 4. 依托海量业务数据，探索并落地各种前沿技术（如预训练、自监督学习、小样本学习等）。
职位要求
1. 本科或以上学历，计算机、自动化、电子信息或相关专业；
2. 熟悉常见机器学习和深度学习相关技术和算法（如分类、聚类、回归等），熟悉至少一种常用深度学习框架（如pytorch、tensorflow等），具有扎实的基础；
3. 有丰富的机器学习相关经验，符合以下至少一个方面： （1） 熟悉计算机视觉相关任务，对图像/视频分类、目标检测、图像/视频检索、OCR、图像分割等至少一方面有经验； （2）熟悉NLP相关任务，对文本分类、语义分析、情感分析、NER等至少一方面有经验； （3）熟悉音频相关任务，对ASR、AED、LID等至少一方面有经验； （4）熟悉多模态学习、大规模预训练等； （5）加分项：熟悉图神经网络、知识图谱的理论和应用，并具有相关经验；熟悉剪枝、量化、蒸馏等模型加速技术，具有使用tensorrt等部署模型相关经验；
4. 编程基础扎实，熟悉基本的数据结构和算法；
5. 具备优秀的分析及解决问题能力、逻辑思维能力、沟通协作能力，保持对新事物的好奇心，责任心强，诚信可靠；
6. 在AI顶会顶刊发表过高水平论文的优先。
（新加坡）
职位描述
1. Be responsible for business content understanding of ads, e-commerce, short video, live streaming, and other related content understanding, including images, text, video, audio, etc. 2. Be responsible for data mining, feature engineering, and building machine learning models to build monetization ecology. 3. Optimize model computation efficiency and improve model stability when facing tens of millions of business data and restricted resources. 4. Based on billion scale business data, explore and implement various cutting-edge technologies, such as pre-training, self-supervised learning, few-shot learning, etc.
职位要求
1. Bachelor's degree or above, majoring in Computer Science, Computer Engineering, Electrical Engineering, or other related fields.
2. Have a solid foundation with common machine learning and deep learning related techniques and algorithms (e.g. classification, clustering, regression, etc.). Be proficient with at least one deep learning framework (e.g. PyTorch, TensorFlow).
3. Be familiar with computer vision related tasks. Have rich experience in at least one aspect, such as image/video classification, object detection, image/video retrieval, OCR, image segmentation, etc.
4. Related experience in at least one of the following areas is a plus:
- 1. Be familiar with NLP-related tasks. Have experience in at least one aspect, such as text classification, semantic analysis, sentiment analysis, NER, etc.
- 2. Be familiar with audio-related tasks. Have experience in at least one aspect, such as ASR, AED, LID, etc.
- 3. Be familiar with multimodal machine learning, large-scale pre-training, etc.
- 4. Be familiar with the theory and application of graph neural networks, knowledge graphs, and have relevant experience;
- 5. Be familiar with model acceleration techniques such as pruning, quantization, distillation, etc.; Have relevant experience in deploying models using frameworks such as TensorRT.
5. Solid programming foundation. Be familiar with basic data structures and algorithms.
6. Have excellent analytical and problem-solving skills, logical thinking skills, communication and collaboration skills. Maintain curiosity about new things, and have a strong sense of responsibility, integrity and reliability.
7. Having published papers in top AI conferences or journals is a plus.
日常实习生要求具有较好的机器学习基础，涉猎方向为CV、NLP、多模态、图网络、音频之一，有较高的主动性和较强的动手能力。实习时间没有硬性规定，6个月以上更好，因为这样更容易做出更完整的成果。若想了解更多信息，欢迎在邮件中说明并留下联系方式。
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