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Open nowPosted 51 days ago

世界模型算法研究员

li/referral788 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now世界模型算法研究员li/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. li/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. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 51 days ago

li/referral median: 1 days open

The posting

视频生成基座,参与自动驾驶多模态视频生成基座模型的研发,覆盖数据 pipeline、tokenizer / VAE、DiT 架构设计、大规模预训练与后训练,并推动 scaling 上的真实收益。 可交互世界模型,研究 action-conditioned / 可控生成,攻克长时序一致性、可干预性与因果可控性;探索几何与语义约束(3D Occupancy、3DGS、Scene Graph)与生成模型的融合方式。 推理加速与规模化可用性,推动生成模型的推理效率优化,包括一致性 / 蒸馏、few-step 采样、量化与算子层优化,把世界模型的采样成本压到数据合成与仿真规模化可用的量级。

计算机视觉、机器学习、计算机科学等相关专业硕士及以上学历,博士优先;毕业 3 年以内(在校期间的长期实习经历可计入相关经验)。 以下两类经验至少具备其一,且是深度参与而非调用或下游微调: 视频 / 图像生成基座模型的训练经验——对数据配比、训练稳定性、loss 异常排查、评测体系有第一手判断; 交互式 / 动作可控世界模型经验——自动驾驶、具身智能、游戏方向均可,理解长时序一致性与可控性的真实难点在哪。 深度学习基础扎实,熟悉 Transformer、Diffusion / Flow Matching、DiT、时空建模。 熟练 Python + PyTorch,具备多机多卡训练经验,熟悉 FSDP / DeepSpeed / Megatron 等至少一种并行训练框架;能独立跑通"数据 → 训练 → 评测 → 迭代"的完整链路。 具备 research insight:能自己定义问题、设计对照实验、快速证伪,在方向不确定时依然能持续推进;自驱性强。 良好的沟通协作能力,能与感知、仿真、规控、强化学习等跨职能团队紧密配合。

加分项 顶会一作论文(CVPR / ICCV / ECCV / NeurIPS / ICLR / ICML / SIGGRAPH 等),或持续保持对前沿方向的判断与产出 参与过知名视频生成 / 世界模型开源项目,或维护过有影响力的开源仓库 有学习式仿真 / 可控生成系统的构建经验:场景合成、长尾场景挖掘与放大、反事实或干预式评测 3D 重建、NeRF、3DGS、Occupancy 相关研究或工程经验 CARLA、NVIDIA Drive Sim 等仿真平台的使用或二次开发经验 生成模型推理加速经验(蒸馏、few-step、量化) Kaggle 等竞赛获奖

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