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Open nowPosted 1391 days ago

大模型训练框架研发工程师-生成模型方向

vrfi1sk8a0/referral187 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型训练框架研发工程师-生成模型方向vrfi1sk8a0/referral · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. vrfi1sk8a0/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 1391 days ago

vrfi1sk8a0/referral median: 1 days open

The posting

你将加入一支把“把万卡集群跑到极致”当日常的团队，与业内领先的通用大模型一起成长。你的核心任务是：突破大规模训练的速度、成本与稳定性的极限。 你将负责的核心工作： 1. 大规模分布式训练系统研发（Scale Matters） - 主导 Data / Tensor / Pipeline / Expert 并行 策略的研发与优化。 - 优化高性能通信、计算通信掩盖、显存复用，提升近万卡训练的集群利用率。 - 面向上百亿〜万亿参数模型，实践高效并行范式。 2. 性能极限优化（Performance Engineering） - 系统性分析训练吞吐瓶颈（comm / compute / IO / kernel / memory）。 - 针对关键路径做深度优化：算子融合、精度压缩、通信调度、显存管理策略。 - 与推理体系协同推进量化、MoE、Speculative Training 等最新机制的训练端落地。 3. 前沿训练技术探索（Frontier Research → Engineering） 根据你的兴趣及特长，可主导其中一类： - 前沿训练范式：Agentic RL、异步 RL等机制的系统工程化落地。 - 训练稳定性：大规模优化器、 动态容灾、divergence 检测与修复机制。 - 可扩展性研究：scaling law 工程落地、模型分布式形态设计、训练效率极限建模。 4. 与硬件深度协作（System × Model） - 与底层 kernel / runtime / compiler 团队协作，共同推进 compute-bound 和 comm-bound 场景的极限性能。 - 设计充分压榨 GPU/NPU 的训练模式，让硬件“跑在它没想过的速度”。

我们希望你具备（不求全，但求强） 1. 可以 0 优化经验，但在其他领域有过优秀成果。 2. 熟悉高级编程语言，具备扎实的数据结构、并行编程、系统基础。 3. 对 PyTorch、DeepSpeed、Megatron、Horovod、JAX、XLA 等之一有体系化理解。 4. 对以下任一方向有深入经验或潜力： - 大型分布式训练（DP/TP/PP/MoE） - GPU/多机通信优化（NCCL、RDMA、通信拓扑） - 训练框架/执行引擎研发 - 大模型训练的性能与稳定性优化 5. 对前沿训练问题有明确兴趣，如： - 训练端：scaling law、混合并行策略、优化器系统、极致显存压缩 - 推理端协同：量化训练、KV cache aware training、speculative training 6. 有大规模模型训练经验（千卡〜万卡）者优先。 7. 有顶会论文、开源贡献、或工程项目能展示你能力者，是加分项但非必要。 为什么加入我们 - 你将直接参与 领先通用大模型的核心训练体系，工作成果会快速转化为真实产品力。 - 足够高的解决问题自由度，我们的工作不仅局限于特定scope，也不受限于特定工程算法方案，只要能够挑战更高的极限，可以放下当前所有的约束。 - 和有“偏执工程美学”的伙伴一起，把“更快、更稳、更大规模”做成一种乐趣。

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