
莉莉丝游戏高级算法工程师
社招全职5年以上算法模型地点:上海状态:招聘
任职要求
1. 计算机、数学、统计学、人工智能等相关专业本科及以上学历; 2. 5 年及以上算法研发相关工作经验,具备扎实的机器学习、深度学习理论基础; 3. 精通常用推荐、广告投放模型与算法及召回/排序相关方法; 4. 具备优秀的工程化能力,熟练使用至少一种深度学习框架(TensorFlow、PyTorch等),有大规模分布式计算平台(Spark、Flink等)经验; 5. 具备广告投放、推荐系统或游戏运营相关的算法实战经验优先; 6. 具备良好的团队管理能力、项目推进能力及跨团队沟通协作能力; 7. 对新技术保持敏锐度,能够结合业务快速落地并产生实际价值。
工作职责
1. 负责算法团队的日常管理与建设,制定团队发展规划与技术方向; 2. 参与并主导广告投放相关业务的模型设计、算法研发与迭代优化; 3. 针对游戏内精细化运营场景,设计并实现高效的推荐算法体系; 4. 推动算法方案在业务中的工程化落地,提升模型的性能与稳定性; 5. 跟踪算法领域前沿技术,并结合业务需求进行创新与优化。
包括英文材料
学历+
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
推荐系统+
[英文] Recommender Systems
https://www.d2l.ai/chapter_recommender-systems/index.html
Recommender systems are widely employed in industry and are ubiquitous in our daily lives.
相关职位
社招5年以上A118837
1. 通过对海量车辆运行日志的深度解析,提取关键信息,包括车辆故障码、传感器数据、驾驶行为数据等,为故障诊断提供数据支持。 2. 运用数据挖掘技术,如聚类分析、关联规则挖掘等,发现车辆日志中的潜在模式和异常行为,提前预警潜在故障风险,为预防性维护提供依据。 3. 构建车辆故障诊断方案检索系统,基于车辆故障特征和历史维修记录,快速检索出与当前故障相似的诊断方案和维修案例,为诊断人员提供参考。 4. 运用大语言模型、机器学习算法,优化存量远程诊断案例方案推荐,针对存量方案库生成新的方案,提高诊断效率和准确性。
更新于 2025-05-26
社招5年以上A35523
1、负责端侧CV算法的研发和落地,包括但不限于目标检测、识别、跟踪等算法; 2、负责算法工程化,包括模型工程化和优化等工作; 3、负责端侧算法框架设计开发; 4、可能也会参与一部分多模态大模型相关的工作;
更新于 2025-04-02