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Open nowPosted 47 days ago

算法实习生

agirobot/internrecruitment367 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now算法实习生agirobot/internrecruitment · Shanghai, 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 agirobot/internrecruitment'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.

12.1% of postings close within 7 days. Measured by our own scanner across the market. agirobot/internrecruitment postings stay open a median of 2 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 47 days ago

agirobot/internrecruitment median: 2 days open

The posting

1. 多模态4D数据全链路建设：搭建仿真（Isaac-sim/UE/Blender）+实机（相机、IMU、双目深度相机）双数据源自动化数据流水线，完成数据采集、解析、清洗、抽帧、标注、质检全流程迭代，构建数据、训练、评测、反馈的完整闭环，支撑机器人感知模型迭代。 2. 4D时空真值自动化标注研发：搭建人体/手部骨骼、SLAM位姿、双目深度重建时空真值自动生产体系，开发自动化标注工具；基于VLM多模态大模型实现标签校验、质检、caption生成自动化，降低人工依赖，输出高质量SMPL人体、手势、三维重建训练数据。 3. 高价值难样本主动挖掘：基于模型失败案例、实机运行问题，搭建「规则+大模型」双驱动数据挖掘产线，精准挖掘长尾场景、边界case及难样本；针对姿态误差、SLAM漂移、重建失效等模型短板，制定定向数据增强与补充方案，输出数据分析报告指导数据采集规划。 4. 数据-模型协同迭代与评测：协同算法团队，量化数据质量、数据多样性对感知模型效果的影响；搭建仿真+实机结合的闭环评测体系，定义位姿、重建精度、深度误差等标准化评测指标，实现问题样本回灌与模型迭代优化。 5. 工具链工程落地优化：研发并优化大规模多模态数据处理Pipeline，结合CUDA、批量推理优化提升流水线效率；负责闭环工具链部署运维，排查链路故障，保障数据生产、标注、挖掘体系稳定高效运转。 【任职要求】 1. 学历与基础能力：计算机、机器人、机器学习、模式识别等相关专业；具备扎实的深度学习、机器学习理论基础，熟练Python/PyTorch，精通数据驱动的模型迭代闭环逻辑，有大规模多模态数据处理、主动学习、难样本挖掘实战经验。 2. 核心技术栈：熟悉人体姿态、手部关键点、视觉SLAM、双目三维重建任一方向；掌握Python/C++，熟悉Linux开发、Shell、Git；了解ROS/ROS2、mcap等机器人数据格式，具备图像、视频、点云、时序数据处理能力。 3. 工程实操能力：熟练使用Isaac-sim/UE/Blender任一仿真引擎，可独立设计仿真数据生产方案；优先掌握VLM大模型批量推理、自动标注、数据筛选功能开发，有4D时空真值、SMPL人体/手部、SLAM数据生产标注经验者优先。 4. 综合素养：具备优秀的问题分析与闭环决策能力，可跨团队协同推进项目落地；自驱力强，能够独立解决复杂技术问题，适配高压迭代节奏。 【加分项】 1. 有大规模4D人体、机器人多模态数据集搭建与自动化质检落地经验； 2. 精通多模态大模型Prompt工程、分布式GPU推理优化； 3. 具备具身智能、机器人感知数据-模型闭环落地项目经验； 4. CVPR/ICCV/ECCV等顶会数据集相关论文发表经历，或拥有高星开源数据工具项目

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