心动TapTap 模型推理优化工程师（上海）
社招全职技术大类地点：上海状态：招聘
任职要求
1.计算机科学、数据科学或相关专业，熟练掌握C++、Python等至少一门编程语言； 2.有丰富的模型训练、推理优化经验，熟悉CUDA，ROCM，OpenCL技术，有基于GPU结构性能调优的经验。 3.熟悉至少一种深度学习框架（Tensorflow/Pytorch/MXNet等），对其底层原理有深入研究。 4.熟悉推理优化常用技术，如特征存取、算子融合、模型并行、流水线、模型量化、混合精度等，有相关工作经验者优先。 5.熟悉DeepSpeed, Megatron等大模型分布式训练框架，对模型优化技术有深入研究的优先； 6.了解主流 AIGC 算法模型原理，有AIGC模型加速优化经验优先。 加分项： 熟悉深度学习框架和底层实现； 熟悉 CV / NLP /推荐系统相关的深度学习算法； 对深度学习训练和推理模型调试、调优有实操经验； 熟悉 TVM 、MLIR 、XLA 等模型优化工具； 有研究生或博士阶段的计算机系统方向（包含分布式系统、并行算法、编程语言与编译器、RDMA 网络、存储、高性能计算等）研究背景； 有软硬件联合设计，底层性能优化（Cutlass ，NCCL 等）的经验； 贡献过开源社区代码。
工作职责
1.负责 TapTap 离线训练、在线推理框架的优化与开发，服务于公司各个业务线，如搜索、推荐、广告、AI 等业务； 2.与公司各算法部门深度合作，分析业务性能瓶颈和系统架构特征，软硬件结合优化，实现极致性能； 3.设计和实现机器学习相关的基础设施/算法框架/工具链等，并推动落地到业务中； 4.探索业界前沿的机器学习相关技术，持续提升平台能力，降低算法使用成本。
包括英文材料
数据科学+
https://roadmap.sh/ai-data-scientist
Step by step roadmap guide to becoming an AI and Data Scientist
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文，免费，零起点，完整示例，基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
CUDA+
https://developer.nvidia.com/blog/even-easier-introduction-cuda/
This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA.
https://www.youtube.com/watch?v=86FAWCzIe_4
Lean how to program with Nvidia CUDA and leverage GPUs for high-performance computing and deep learning.
OpenCL+
https://developer.nvidia.com/opencl
OpenCL™ (Open Computing Language) is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs.
https://engineering.purdue.edu/~smidkiff/ece563/NVidiaGPUTeachingToolkit/Mod20OpenCL/3rd-Edition-AppendixA-intro-to-OpenCL.pdf
we will give a brief overview of OpenCL for CUDA programers.
[英文] Hands On OpenCL
https://handsonopencl.github.io/
An open source two-day lecture course for teaching and learning OpenCL
https://leonardoaraujosantos.gitbook.io/opencl/chapter1
Open Computing Language is a framework for writing programs that execute across heterogeneous platforms.
https://ulhpc-tutorials.readthedocs.io/en/latest/gpu/opencl/
OpenCL came as a standard for heterogeneous programming that enables a code to run in different platforms.
https://www.youtube.com/watch?v=4q9fPOI-x80
This presentation will show how to make use of the GPU from Java using OpenCL.
性能调优+
https://goperf.dev/
The Go App Optimization Guide is a series of in-depth, technical articles for developers who want to get more performance out of their Go code without relying on guesswork or cargo cult patterns.
https://web.dev/learn/performance
This course is designed for those new to web performance, a vital aspect of the user experience.
https://www.ibm.com/think/insights/application-performance-optimization
Application performance is not just a simple concern for most organizations; it’s a critical factor in their business’s success.
https://www.oreilly.com/library/view/optimizing-java/9781492039259/
Performance tuning is an experimental science, but that doesn’t mean engineers should resort to guesswork and folklore to get the job done.
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.
MXNet+
https://www.tutorialspoint.com/apache_mxnet/index.htm
Apache MXNet is a powerful deep learning framework that supports both symbolic and imperative programming.
DeepSpeed+
https://www.youtube.com/watch?v=pDGI668pNg0
Megatron+
https://www.youtube.com/watch?v=hc0u4avAkuM
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
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/
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
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.
分布式系统+
https://www.distributedsystemscourse.com/
The home page of a free online class in distributed systems.
https://www.youtube.com/watch?v=7VbL89mKK3M&list=PLOE1GTZ5ouRPbpTnrZ3Wqjamfwn_Q5Y9A
AIGC+
https://ui.adsabs.harvard.edu/abs/2023arXiv230406632W/abstract
To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged.
NCCL+
https://developer.nvidia.com/nccl
The NVIDIA Collective Communication Library (NCCL) implements multi-GPU and multi-node communication primitives optimized for NVIDIA GPUs and networking.
相关职位
社招技术大类
1.负责 TapTap 离线训练、在线推理框架的优化与开发，服务于公司各个业务线，如搜索、推荐、广告、AI 等业务； 2.与公司各算法部门深度合作，分析业务性能瓶颈和系统架构特征，软硬件结合优化，实现极致性能； 3.设计和实现机器学习相关的基础设施/算法框架/工具链等，并推动落地到业务中； 4.探索业界前沿的机器学习相关技术，持续提升平台能力，降低算法使用成本。
更新于 2025-08-14
社招技术大类
1. 研发面向游戏平台场景的多模态大模型，包括文本、图像、视频、音频等多模态数据的理解与生成； 2. 构建与优化游戏内容理解算法，涵盖游戏玩法解析、类型识别、特色标签生成、玩家评论分析、剧情/世界观理解等； 3. 参与 TapTap 风格的视频与广告素材生成方案，支持用户增长团队等多模态素材生成的训练需求； 4. 支持游戏搜索与发现，利用多模态大模型进行精准匹配、语义搜索、跨模态检索（文本→视频、视频→游戏等）； 5. 跟进并引入最新的多模态大模型研究成果，探索其在游戏行业的新应用场景； 6. 有预研的空间，进行学术研究与论文发表，推动顶会/顶刊产出，并结合业务进行技术落地； 7. 优化大模型的训练与推理性能，保障在海量游戏内容和用户数据上的高效运行。
更新于 2025-08-15
社招技术大类
1. 研发面向游戏平台场景的多模态大模型，包括文本、图像、视频、音频等多模态数据的理解与生成； 2. 构建与优化游戏内容理解算法，涵盖游戏玩法解析、类型识别、特色标签生成、玩家评论分析、剧情/世界观理解等； 3. 参与 TapTap 风格的视频与广告素材生成方案，支持用户增长团队等多模态素材生成的训练需求； 4. 支持游戏搜索与发现，利用多模态大模型进行精准匹配、语义搜索、跨模态检索（文本→视频、视频→游戏等）； 5. 跟进并引入最新的多模态大模型研究成果，探索其在游戏行业的新应用场景； 6. 有预研的空间，进行学术研究与论文发表，推动顶会/顶刊产出，并结合业务进行技术落地； 7. 优化大模型的训练与推理性能，保障在海量游戏内容和用户数据上的高效运行。
更新于 2025-09-28
实习技术大类
1. 研发面向用户增长场景的多模态大模型，包括文本、音频、图像、视频等多模态数据的理解与生成； 2. 跟进并引入最新的多模态大模型研究成果，探索其在游戏行业的新应用场景； 3. 优化大模型的训练与推理性能，保障在海量游戏内容和用户数据上的高效运行。
更新于 2025-10-13