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Open nowPosted 43 days ago

后训练Infra工程师

dcar/referral674 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now后训练Infra工程师dcar/referral · Beijing, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on dcar/referral's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

11.8% 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. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 43 days ago

The posting

岗位定位：建设后训练框架与平台，提升训练效率、稳定性和易用性，支撑算法迭代与 Agent 业务落地。

岗位职责： 1、平台建设：统一 SFT、DPO、GRPO 等训练流程，完善任务编排、资源调度、环境与产物管理、实验追踪和故障恢复； 2、Agent RL 基建：建设 rollout、沙箱及工具交互环境，支持多轮交互、异步采样与训推协同； 3、性能与稳定性：优化显存、通信、吞吐及资源利用率，解决 hang、OOM、训练异常等问题，提升有效训练时间占比与任务成功率； 4、数据与评测管线：集成数据处理、评测和回归能力，打通业务数据、训练、评测与问题反馈闭环。

1、熟悉大模型训练，有 Megatron、DeepSpeed、Swift、verl 等框架的深入使用或二次开发经验； 2、理解分布式并行、NCCL 通信、显存管理及 checkpoint 机制，有实际性能调优经验； 3、系统基础扎实，熟悉性能分析与分布式调试，能独立定位训练稳定性问题； 4、理解或有意深入 RL 后训练系统，关注 rollout、沙箱、训推一致性等问题； 加分项 5、有 RLHF / Agentic RL、数据管线或训练评测平台建设经验； 6、有千卡级集群稳定性、有效训练时间或成本优化成果； 7、有训练、推理框架的实质性开源贡献。

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