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Open nowPosted 73 days ago

云端静态感知与建图算法工程师

li/referral788 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now云端静态感知与建图算法工程师li/referral · 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 li/referral's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

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. li/referral postings stay open a median of 1 days.

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 73 days ago

li/referral median: 1 days open

The posting

负责自动驾驶云端静态感知与建图算法开发与优化，面向车道线、路沿、可行驶区域、路口拓扑、静态障碍物等地图要素，支撑地图生产与环境理解能力建设。 负责数据真值处理、样本构建、模型训练、评测分析及 badcase 闭环迭代，持续提升复杂场景下的精度、稳定性、泛化能力和整体迭代效率。 负责训练与推理链路的工程落地，包括部署、性能优化、资源控制和版本回归，推动数据、模型与服务全链路优化。 参与静态环境统一建模、多任务协同及大模型辅助数据闭环、地图理解等方向的技术探索，推动云端静态感知与建图方案持续演进。

本科及以上学历，计算机、人工智能、自动化、机器人等相关专业。 有自动驾驶感知、建图、BEV、3D 感知、点云处理、地图要素提取等相关经验。 熟悉 PyTorch 训练与调优流程，具备较强的问题分析、实验设计和迭代推进能力。 具备较强工程落地意识，能够胜任数据处理、评测分析、部署优化和问题闭环等实际工作。 有自动驾驶量产经验、推理部署经验、badcase 分析经验者优先。 对多模态学习或大模型在数据闭环、地图理解中的应用有兴趣或实践经验者优先。

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  1. A CV for this posting

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  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on li/referral's form

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    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    li/referral's answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

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  6. The interview in your calendar

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    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
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3×

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  • How it was written, and how the review scored it
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  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

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