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Open nowPosted 8 days ago

Omni 预训练研究员

agirobot/index77 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowOmni 预训练研究员agirobot/index · Shanghai, Chinese Mainland

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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 8 days ago

The posting

面向打造竞争力领先的全模态交互基础模型，参与全模态基模的架构设计与预训练/后训练，研究跨模态表示、融合和训练方法；与数据、Infra 团队协作，提升模型能力、训练稳定性及迭代效率。 1、研究全模态基模架构与表示方法，包括视觉与音频编码、离散或连续模态表示、共享骨干与模态专用组件，以及生成端建模与解码； 2、设计预训练与后训练方案，研究多模态训练目标、分阶段训练、参数初始化及优化策略；开展模型规模、数据规模与计算预算的对照实验，提升收敛稳定性和训练效率； 3、与数据团队共同设计数据配比、采样策略与课程学习方案，通过消融实验量化数据、架构和训练策略对各模态能力及跨模态迁移的影响，指导数据迭代； 4、建设模型评测与诊断方法，覆盖图文理解、语音理解与生成、视频时序理解及跨模态推理，评估生成质量与跨模态一致性；分析模态能力失衡、幻觉和遗忘等问题； 5、跟踪并复现前沿全模态研究，沉淀可复现的实验与训练方案；与 Infra 团队协作解决训练异常和性能瓶颈，通过必要的后训练实验验证基模能力向下游任务的迁移。

1、计算机、人工智能、电子信息、数学等相关专业本科及以上学历，具备扎实的深度学习基础和独立设计、分析实验的能力； 2、熟悉大模型训练原理，熟练掌握 Python、PyTorch，能够实现和修改模型结构、损失函数及训练流程； 3、具备大模型预训练、持续预训练或多模态预训练项目经验，能够结合损失曲线、梯度和数值异常分析收敛问题，开展有针对性的诊断实验； 4、熟悉视觉、语音、视频至少一个方向的表示学习与建模，了解跨模态对齐或融合，具备论文阅读、复现和系统实验能力，能够与数据、工程团队协作； 5、有全模态基模训练相关经验者优先；有高质量论文、开源项目或可验证的训练成果者优先。

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