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Open nowPosted 61 days ago

Post-Train Infra开发工程师

dcar/referral674 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowPost-Train Infra开发工程师dcar/referral · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 61 days ago

The posting

团队定位：为公司模型迭代提供基建与保障，降本增效——降低算力成本，提高模型开发效率。 岗位定位：开发并优化 Post-Train（后训练）框架与基础设施，把环境、框架、稳定性等收进 infra，让算法专注于实验与业务效果本身。

岗位职责： 1、开发并优化 Post-Train 框架，包括但不限于：RL rollout 运行沙箱集群、Agentic RL 多轮调度、RL 训推不一致治理等，持续降低后训练的使用门槛； 2、训练效率优化：深入训练全链路，覆盖显存优化、吞吐与延迟优化、通信加速等手段，提升训练综合 ETTR 这一核心指标，提升资源整体利用率； 3、训练稳定性保障：解决算法同学遇到的通信 hang、OOM、框架参数、模型结构不支持、算子版本不支持等问题，保证训练任务的成功率； 4、跟进 SFT / DPO / GRPO 等后训练范式的工程落地，抽象通用组件沉淀到统一训练框架中。

1、熟悉大模型训练原理与流水线，有 Megatron / DeepSpeed / Swift 等框架的深入使用或二次开发经验； 2、理解分布式训练核心技术：数据/张量/流水线并行、NCCL 通信、显存管理、混合精度，有实际的性能调优经验； 3、有扎实的系统功底，能定位 hang、OOM、性能劣化等疑难问题，熟悉 nsys / 火焰图 / 日志链路排查手段； 4、对 RL 后训练（PPO / GRPO / Agentic RL）的系统架构有理解或强烈兴趣，了解 rollout 与 training 的协同模式； 5、自驱、能啃硬问题，愿意长期跟踪训练框架与系统方向的前沿进展。 加分项： 1、有 RLHF / Agentic RL 系统（rollout 集群、环境沙箱、异步采样调度）建设经验； 2、有大规模训练（千卡以上）稳定性保障或 ETTR 优化的量化成果； 3、在训练框架开源社区（Megatron / DeepSpeed / Swift / verl 等）有实质性贡献。

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