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Open nowPosted 265 days ago

AIGC图像生成算法工程师

kaonai/index37 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAIGC图像生成算法工程师kaonai/index · 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.

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 265 days ago

The posting

  • 基于业务目标与产品需求，负责团队图像生成/编辑模型与端到端多模态统一模型的训练迭代与落地，包括但不限于 SFT、偏好对齐 / RL（RLHF / DPO / GRPO 等）。
  • 负责端到端多模态原生统一模型（理解 + 生成一体）的训练与工程化：图文交错序列建模、统一 token 空间、理解–生成联合训练、原生长上下文与长程一致性的实现与优化。
  • 负责训练数据全流程建设：数据收集与清洗、质量评估、去重与过滤、难例挖掘、数据配比与版本管理，构建可持续迭代的数据体系与规范。
  • 负责训练管线与工程化建设：分布式训练（DeepSpeed/FSDP 等）、显存/吞吐优化、checkpoint 管理、训练监控与故障排查，持续提升训练效率与稳定性。
  • 设计并实现图像/多模态生成质量评估体系：自动指标（CLIP/美学/一致性/可控性/长程一致等）+ 人工评测流程，建立「评测→诊断→迭代」的闭环。
  • 跟进前沿论文与开源生态（Diffusers/ComfyUI/SD 系列、DiT/Flow Matching，以及自回归多模态、图文交错生成、统一理解-生成模型、长上下文推理/serving），结合业务场景完成复现、改进、实验对比与 A/B 验证。
  • 跨团队协作（产品/工程/运营），将模型能力沉淀为可复用的训练策略、数据策略与最佳实践文档，推动生产化交付。
  • 计算机/数学/统计/相关专业本科及以上学历；具备扎实的机器学习与深度学习基础。
  • 熟悉 PyTorch 生态，具备实际训练与调参经验；能独立阅读论文与代码，完成复现、改造与工程化落地。
  • 对图像生成 / 编辑模型（文生图 / 图生图，如 Qwen-Image / Qwen-Image-Edit、扩散模型 / DiT / Flow Matching / VAE / ControlNet / LoRA 等） 至少一类有深入训练或应用经验。
  • 熟悉至少一种对齐/偏好优化方法并能实际落地：SFT、DPO/IPO、RLHF/GRPO、reward modeling、pairwise preference 等。
  • （加分）了解或有实践经验于端到端多模态原生统一模型 / 自回归多模态（图文交错生成、统一理解-生成，如 SenseNova-U1 类架构），或长上下文 / KV / 多模态 serving。
  • 具备良好的工程习惯：可复现实验、规范记录、数据与模型版本管理（DVC/MLflow/W&B 等思想或工具）、问题定位与性能优化能力。
  • 沟通与协作能力强，能把「业务目标」转译成「可验证的训练目标、指标与实验设计」，并推动结果落地。

加分项 - 有多卡/分布式训练经验（DeepSpeed/FSDP/Accelerate），或有训练提速/省显存经验（FlashAttention、gradient checkpointing、offload 等）。 - 有评测体系建设经验：人评流程设计、标注指南、评测平台搭建、指标与业务目标对齐。 - 有高质量开源贡献 / 竞赛经历 / 相关论文或技术博客；能持续输出可复用的方法论与工具链。 - 熟悉图像生成产品链路（提示词系统、风格/人物一致性、编辑能力、分镜/连续一致性等），能从产品问题抽象成训练/数据/评测方案。 - 熟悉多模态统一模型（理解+生成一体、图文交错、原生长上下文、长程一致）的训练 / 推理 / serving 者优先。

我们能提供 - 直接参与核心业务的模型迭代与生产落地，配套真实线上指标与 A/B 环境。 - 完整的「数据 → 训练 → 评测 → 上线」闭环与资源支持（训练数据、算力资源、协作机制）。 - 清晰的成长路径：训练工程 / 对齐 / 评测体系 / 多模态理解-生成统一方向的深入发展机会，推动更大规模的生成式与多模态体系建设。

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