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Open nowPosted 109 days ago

端到端大模型算法工程师(Mstar)

momenta/campus30 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now端到端大模型算法工程师(Mstar)momenta/campus · 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 momenta/campus's own form.

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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 109 days ago

The posting

1. 参与端到端自动驾驶大模型的架构设计与核心算法研究，整合感知、预测与规划模块，推动模型整体性能持续提升； 2. 基于Transformer、VLM等主流架构，探索感知-规划一体化建模方法，提升模型在复杂、长尾驾驶场景下的泛化与鲁棒性； 3. 结合Momenta海量量产路测数据，设计数据闭环驱动的训练策略，构建高质量数据集，加速模型迭代； 4. 追踪自动驾驶和人工智能领域等相关领域最新研究进展，探索前沿技术在端到端驾驶模型中的落地应用。 5. 基于端到端大模型，进行世界模型算法预研，提升端到端模型效果和泛化性。

1. 计算机、人工智能、自动化等相关专业硕士/博士应届毕业生； 2. 扎实的深度学习基础，熟悉Transformer、ViT、LLM等主流模型架构； 3. 具备VLM或VLA方向的研究或工程经验，理解视觉-语言对齐、多模态建模等核心技术； 4. 了解强化学习基本原理（PPO、GRPO 等），有RL在决策或控制任务中应用经验者优先； 5. 熟练掌握Python及PyTorch等主流训练框架，具备独立复现论文与工程化落地能力； 6. 在 CV、多模态或自动驾驶相关方向有研究或实习经历，有顶会论文（CVPR、ICCV、NeurIPS、ICLR 等）者优先。 加分项： 1. 有端到端自动驾驶、BEV 感知、运动预测或规划相关研究/工程经验； 2. 了解强化学习、世界模型或生成式方法在驾驶场景中的应用； 3. 有大规模分布式训练经验（Megatron-LM、DeepSpeed、vLLM 等）。

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