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Tencent Holdings · Beijing

# 浏览器数据后处理算法工程师

Beijing 

Listed on Tencent Holdings’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Tencent Holdings’s careers site →](https://careers.tencent.com/en-us/jobdesc.html?postId=2100835549295325184)

[OpenNash Jobs](https://jobs.opennash.com/) / [Tencent Holdings](https://jobs.opennash.com/companies/tencent-holdings/) / 浏览器数据后处理算法工程师

## Job description

### Responsibilities
- 针对浏览器端/云端协同交付场景，开发并优化包括网页长文本/复杂结构清洗、核心正文解析抽取、智能切片与摘要生成、元数据提取等关键能力，提升端到端处理质量与时延表现；
- 深入探索小模型（1B~7B）的技术上限，在 Efficient Fine-Tuning、SFT、DPO/RLHF 对齐、模型量化压缩及长文本上下文扩展等方向持续迭代，探索端侧/低算力部署的最优解；
- 联合数据团队搭建高质量后处理语料库构建流程，通过 Synthesizing Data（合成数据）与 Reward Model 精选数据集，持续提升模型在复杂变体网页上的泛化能力。 Requirements

### 专业背景
- 计算机、人工智能、软件工程或数学相关专业，本科及以上学历，3 年以上 NLP / LLM 算法开发经验；

### 小模型后训练经验
- 深入理解大/小语言模型架构，具备丰富的小模型（如 Llama, Qwen, Phi, Gemma 等系列）全流程 Post-training（预训练增量/SFT/DPO/PPO）实战经验，熟悉高效微调（LoRA, QLoRA）与分布式并行训练框架（DeepSpeed, Megatron-LM, Ray 等）；

### 数据处理能力
- 具备大规模海量数据（PB级/数十亿网页级）清洗与构建经验，熟悉网页解析（HTML/DOM Tree 提取、格式转换）及文本去重、过滤、质量评分机制；

### 探索与工程落地
- 具备优秀的技术敏锐度和独立探索能力，能快速跟踪并重现业界最新顶级论文或开源方案；具备良好的 Python/C++ 编码能力，熟悉 PyTorch 生态，能兼顾效果与推理吞吐/延迟性能。

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 12, 2026
- Last checked: Oct 19, 2026
- Listed for: 7 days
- Location: Beijing[Apply on Tencent Holdings’s careers site →](https://careers.tencent.com/en-us/jobdesc.html?postId=2100835549295325184)

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Dates come from the employer where stated; “first seen” and “last checked” are our own observations.

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