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Open nowPosted 184 days ago

大模型训练系统与优化工程师（VLM/Agent RL方向）-Data

ByteDance19,268 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now大模型训练系统与优化工程师（VLM/Agent RL方向）-DataByteDance · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.9% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 9 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 184 days ago

ByteDance median: 9 days open

The posting

1、统一训练架构建设与演进：负责大模型Post-training框架的代码抽象与底层重构，兼容不同模态、不同训练方式的、正确的、高效的训练框架的建设； 2、超大规模分布式训练优化：面向100B~1T参数级别的超大模型，探索并落地极致的分布式训练策略（DP/TP/PP/EP等组合），通过算子融合、显存优化、分布式策略调整等手段大幅提升集群训练的MFU； 3、Agent RL框架与评估底座建设：攻坚Reasoning RL/Agent RL的训练框架，构建适用于复杂业务环境的标准化评测基准与标准的、稳定的Harness，解决千亿模型在RL阶段的稳定收敛与Rollout效率问题，解决在Agent RL下的长尾问题； 4、多模态与新架构支持：为MoE、Linear Attention等新型模型结构，以及多模态（图、文、音、视）复杂模态的模型训练提供高效支持与正确性（Convergence）验证。

1、具备2年以上机器学习系统设计、开发与性能调优经验，熟练掌握Python与C++； 2、深入理解并具备PyTorch、DeepSpeed、Megatron或FSDP等主流分布式训练框架的二次开发经验； 3、有100B以上超大模型分布式训练实战经验，能够独立排查并解决收敛性问题与分布式训练瓶颈； 4、在强化学习训练框架（PPO/GRPO/Agent RL）或大模型评测底座、Agentic Harnes有深度实践者优先； 5、具备工程素养，对提升AI Infra的研发效率、代码整洁度与系统稳定性有追求。

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