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Open nowPosted 39 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

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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 39 days ago

The posting

1、面向端到端自动驾驶大模型，研究并落地RL、On-policy Distillation等后训练范式，提升模型在复杂驾驶场景下的决策质量与泛化能力； 2、结合Momenta海量量产真实路测数据，设计数据闭环驱动的后训练策略，加速模型在长尾驾驶场景中的能力提升； 3、对比和融合SFT、RL、On-policy Distillation等范式，针对驾驶决策任务的特殊性设计最优后训练方案； 4、结合分布式训练框架优化后训练系统效率，推动模型从研究到量产的快速迭代； 5、追踪并复现LLM、端到端自动驾驶领域最新研究成果。

1、计算机、人工智能等相关专业硕士/博士应届毕业生； 2、必须具备Flow Matching相关经验； 3、深入理解后训练核心范式，对以下概念有实质性认知： - On-Policy Vs Off-policy采样的本质区别 - Dense vs Sparse Reward对训练效果与收敛的影响 4、在以下至少一个方向有研究或工程实践经验： - RL后训练（PPO、GRPO、RLHF 等） - On-policy/Sequence-level蒸馏（GKD、MiniLLM、DAGGER 相关方法） 5、熟悉至少一种主流训练框架（VeRL、Megatron-LM、DeepSpeed、vLLM等）； 6、有顶会论文（NeurIPS、ICML、ICLR等）或相关方向实习经历者优先。

加分项 1、同时具备Flow Matching与RL经验； 2、同时具备RL与蒸馏两个方向经验； 3、有Reward Model、Value Model或Token-level Scoring相关研究经验； 4、对自动驾驶决策、具身智能或物理AI场景有了解，理解驾驶轨迹数据与通用NLP数据的本质差异。

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