Organization Library 科技企业

AI实验室

AI Research Lab

以前沿AI研究为核心使命，兼顾论文发表与产品化落地的双轨制研究机构，管理大规模GPU算力资源

🏭 基于此组织类型的 AI 班子

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🧩 拼一个这类组织的班子 →
11
Org Roles
50-200
Typical Size
11
Required Roles
11
Linked Individuals

Mission

推动人工智能前沿研究突破，将研究成果转化为安全、有益的AI产品和服务

Member Roles

实验室主任/CEO

Lab Director / CEO Required C-suite
Headcount: 1 Ref: management/ceo
Core KPIs:
顶会论文数 研究影响力(引用数) 人才吸引力 产品收入增长
Collaborations:
chief-scientist vp-engineering head-of-safety research-leads

首席科学家

Chief Scientist Required C-suite
Headcount: 1 Ref: technology/ai-researcher
Core KPIs:
研究突破数(论文+模型) 学术影响力指数(H-index/引用) 团队技术水平提升
Collaborations:
lab-director research-leads head-of-safety

工程副总裁

VP of Engineering Required C-suite
Headcount: 1 Ref: technology/cto
Core KPIs:
GPU利用率 训练吞吐量 API可用性SLA 基础设施成本效率
Collaborations:
lab-director chief-scientist infra-lead ml-platform-lead

研究组负责人

Research Team Leads Required VP
Headcount: 3-6 Ref: technology/ai-researcher
Core KPIs:
论文发表数和质量 Benchmark表现 人才培养(晋升数) 研究项目推进节奏
Collaborations:
chief-scientist other-research-leads ml-engineers data-team

AI安全负责人

Head of AI Safety Required VP
Headcount: 1 Ref: technology/ai-safety-researcher
Core KPIs:
安全事件数 Red Team发现率 安全研究论文数 合规审计通过率
Collaborations:
lab-director chief-scientist research-leads product-lead

基础设施负责人

Infrastructure Lead Required Manager
Headcount: 1 Ref: technology/cloud-architect
Core KPIs:
集群可用性 GPU利用率 Job等待时间 成本/FLOP
Collaborations:
vp-engineering ml-platform-lead research-leads

ML平台负责人

ML Platform Lead Required Manager
Headcount: 1 Ref: technology/machine-learning-engineer
Core KPIs:
平台工具采用率 训练效率(Tokens/sec) 推理延迟P99 研究员满意度
Collaborations:
vp-engineering infra-lead research-leads product-lead

产品负责人(API与应用)

Product Lead (API & Applications) Required Manager
Headcount: 1 Ref: technology/product-manager
Core KPIs:
API收入 开发者数量增长 API调用量 客户NPS
Collaborations:
vp-engineering research-leads head-of-safety developer-relations

研究科学家

Research Scientists Required Individual Contributor
Headcount: 20-80 Ref: technology/ai-researcher
Core KPIs:
论文数量和质量 Benchmark提升 代码贡献 同行评价
Collaborations:
research-leads ml-engineers data-team

机器学习工程师

Machine Learning Engineers Required Individual Contributor
Headcount: 15-40 Ref: technology/machine-learning-engineer
Core KPIs:
模型上线速度 推理性能优化 训练效率提升 代码质量
Collaborations:
research-scientists ml-platform-lead infra-lead

数据工程师与标注团队

Data Engineers & Annotators Required Individual Contributor
Headcount: 5-15 Ref: technology/data-engineer
Core KPIs:
数据质量评分 标注准确率 Pipeline处理吞吐量 数据合规率
Collaborations:
research-scientists ml-platform-lead head-of-safety
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