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Open nowPosted 9 days ago

模型后训练与在线学习工程师

seer-group/index52 open roles

Where
Hangzhou, Zhejiang, Chinese Mainland
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Your applicationOpen now模型后训练与在线学习工程师seer-group/index · Hangzhou, Zhejiang, Chinese Mainland
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  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 9 days ago

The posting

1. 负责具身模型和机器人策略模型的后训练、微调和性能优化。 2. 负责机器人自主 Agent 的整体架构设计和核心模块实现。 3. 负责训练数据采样、经验回放、策略更新和安全约束。 4. 负责人工接管、失败恢复、纠错轨迹和成功轨迹的数据处理。 5. 负责复杂任务的分解、技能调用、工具调用和任务状态管理。 6. 设计并实现 SFT、行为克隆、DAgger、偏好优化、强化学习和 test-time training。 7. 设计正负样本、困难样本、失败分支和恢复分支的数据配比。 8. 研究机器人在不同构型、动力学、负载和环境条件下的快速适配。 9. 建立训练、评测、模型对比和回归验证流程。 10. 建立 Agent trace、运行日志、任务状态和故障记录体系。 11. 分析模型训练中的负向迁移、灾难性遗忘和任务间干扰。 12. 与 Data Engine、Agent、WAM、Cloud 和 Infra 团队协作实现闭环。 任职要求 1. 计算机、机器学习、机器人或相关专业本科及以上学历。 2. 熟悉监督微调、行为克隆、强化学习、模仿学习、元学习或在线优化。 3. 熟悉 DAgger、RLHF、DPO、PPO、SFT、LoRA 或相关后训练方法。 4. 熟悉 PyTorch、分布式训练、数据加载和实验追踪。 5. 具有 LLM Agent、机器人任务规划、多模态模型或自主系统研发经验。 6. 理解 robot dynamics、partial observability、exploration 和 safety constraint。 7. 具备较强的数据分析、模型诊断和实验设计能力。 8. 能够独立完成训练 recipe 设计、实验迭代和真机验证。 加分项 1. 有真实机器人 post-training、online learning 或 test-time adaptation 经验。 2. 有 ENPIRE 类 rollout—inspect—modify—rerun 经验。 3. 有具身基础模型、VLA、WAM 或多模态大模型经验。 4. 有 in-context learning、context adaptation 或 failure recovery 研究成果。 5. 有安全强化学习、策略回滚或在线模型更新经验。

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