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Open nowPosted 470 days ago

PnC算法工程师

r3c0qt6yjw/neolix58 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowPnC算法工程师r3c0qt6yjw/neolix · Beijing, Chinese Mainland

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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
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  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 470 days ago

The posting

1. 负责L4级别自动驾驶系统中 Planning 算法研发，支撑纯无人车产品大规模运营落地。 2. 面向城市、高速、末端非结构化场景，研发通用的、具备泛化能力，能够规模化部署在各个城市、地区、国家，适应不同的交规与交通文化的无人车相关算法，包含基于概率建模的交互算法与基于数据驱动的e2e模型。 3. 紧跟技术前沿，对于业界最新进展，包括但不限于概率建模方法（MCTS、RACP、Zero-order Optimization）、e2e模型（AR、Diffusion、RL post-train）进行充分的掌握与了解，进行技术选型与判断，帮助团队交付一代、研发一代、预研一代。

职位亮点 1. 我们拥有全球最大的无人车车队、全球部署&运营、海量高质量、多样化路测数据、充足的 GPU 算力集群支撑大规模模型训练。 2. 团队核心成员均来自国内外顶尖自动驾驶公司/大厂，学术氛围浓厚，技术导向，没有 PUA。

1. 本科以上学历，C9/985 更佳、硕士/博士 更佳；计算机、人工智能、数学等相关专业优先；有过打榜 leadboard 经验并取得优异成绩者优先；有过顶会论文（只计算一作）优先。 2. 熟悉优化理论，熟悉 exploration and exploitation 等决策范式；具备良好的算法思维与抽象设计能力，对于基于概率推演建模的算法范式（i.e. MCTS、MARC、RACP）等有实践经验，能够将其应用到具体场景中；了解前沿优化理论如零阶优化、强化学习等范式，能将其应用到具体场景中。 3. 熟悉深度学习，对 planning 业务有深刻理解，能够针对 planning 问题建模特性进行技术选型，熟悉 AR、Diffusion 等模型架构，了解模型的开/闭环评价体系，了解 e2e 里 IL 与 RL 的分工与各自任务目标，及其相应对数据的需求，包含数据标注、采集、数据质量、配比等。能够将主流的 e2e 模型应用到 planning 场景上。 4. 深刻掌握 L4 运营业务、场景，同时了解概率建模方法论与e2e 模型方法论，具备抽象架构能力，能合理设计经典范式与数据驱动范式的结合，达到泛化能力高、解释性强、确定性高，将MPCI 等指标持续提升，完成公司运营目标。 5. 具备很好的学习能力、沟通协作能力；具备利用 agent 完成独立工程的能力，熟悉各种场景下的 skill 选型，熟悉与各种 agent的合作范式，能够通过 agent 完成前沿、创新的工作，持续拓展自己与团队的边界；具备开源精神，能够很好的与上下游、团队内部沟通协调。

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