Collect first agent research and market sources
This commit is contained in:
wuyang
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# Job: 百度 - 2027AIDU 智能体算法工程师
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---
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type: job
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company: 百度
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role: 2027AIDU-智能体算法工程师
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location: 北京市
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source_url: https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE
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source_name: 百度校园招聘
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source_quality: official
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collected_at: 2026-07-08
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posted_at: 2026-05-12
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first_seen_at: 2026-07-08
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last_checked_at: 2026-07-08
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snapshot_type: new
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job_code: J99969
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previous_snapshot:
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status: active
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level: graduate
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team: AIDU项目
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business_area: search / conversation / office / data platform / entertainment
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employment_type: campus
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salary:
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skills:
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- agent
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- planning
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- tool-use
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- memory
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- multi-agent
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- rag
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- agent-evaluation
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topics:
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- agent-architecture
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- evaluation
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- memory
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models:
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-
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related_papers:
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- 2025-vijayvargiya-openagentsafety
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- 2026-memory-agent-survey
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related_experiments:
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-
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related_projects:
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-
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relevance: high
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---
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## Source Snapshot
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- source: 百度官方招聘页
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- collected_at: 2026-07-08
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- availability: 页面可访问，岗位列表显示发布日期 2026-05-12。
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## Change Snapshot
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- first_seen_at: 2026-07-08
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- last_checked_at: 2026-07-08
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- snapshot_type: new
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- previous_snapshot:
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- changed_fields:
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## Raw JD Summary
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百度 AIDU 的智能体算法岗位要求负责 AI Agent 设计和研发，覆盖感知、决策、执行闭环、多智能体协作、长期记忆、推理、规划、工具调用、反思、代码生成、RAG 融合和 Agent 评测体系建设。
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## Responsibilities
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- 设计和研发 AI Agent 的核心机制。
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- 推动 Agent 在搜索、对话、办公、数据平台、网盘文库、生活娱乐等产品场景落地。
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- 建设评测体系，优化成功率、延迟、成本和用户体验。
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## Requirements
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- 熟悉 ReAct、AutoGPT、CoT 等范式。
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- 熟悉 LangChain、AutoGen、CrewAI 等 Agent 框架。
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- 理解 RAG 与 Agent 融合架构。
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## Extracted Signals
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- hard requirements: planning、tool use、memory、multi-agent、RAG、eval。
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- preferred skills: Agent 框架实践、产品场景落地。
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- hidden signals: 百度把 Agent 从单点算法能力看成端到端系统能力。
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- business direction: 搜索、对话、办公、数据平台和泛生活娱乐场景。
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## Knowledge Links
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- concepts: Agent loop, tool use, memory, evaluation
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- papers: OpenAgentSafety, memory survey
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- experiments: eval harness, memory benchmark
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- projects:
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## Gaps for Us
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- 需要补充主流 Agent 框架的横向比较。
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- 需要建立 Agent 评测指标和任务集。
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# Job: 百度 - 2027AIDU Agent 应用全栈工程师
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---
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type: job
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company: 百度
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role: 2027AIDU-Agent应用全栈工程师
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location: 北京市
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source_url: https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE
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source_name: 百度校园招聘
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source_quality: official
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collected_at: 2026-07-08
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posted_at: 2026-05-12
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first_seen_at: 2026-07-08
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last_checked_at: 2026-07-08
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snapshot_type: new
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job_code: J99974
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previous_snapshot:
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status: active
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level: graduate
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team: AIDU项目
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business_area: search / healthcare / enterprise service / data analysis / office automation
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employment_type: campus
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salary:
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skills:
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- agent
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- planning
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- tool-use
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- function-calling
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- memory
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- reasoning
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- state-management
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- multi-agent
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- rag
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- agent-evaluation
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topics:
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- workflow-agent
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- agent-architecture
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- evaluation
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models:
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-
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related_papers:
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- 2026-dialogue-swebench
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- 2026-swe-evo
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related_experiments:
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-
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related_projects:
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-
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relevance: high
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---
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## Source Snapshot
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- source: 百度官方招聘页
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- collected_at: 2026-07-08
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- availability: 页面可访问，岗位列表显示发布日期 2026-05-12。
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## Change Snapshot
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- first_seen_at: 2026-07-08
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- last_checked_at: 2026-07-08
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- snapshot_type: new
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- previous_snapshot:
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- changed_fields:
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## Raw JD Summary
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岗位强调构建下一代 Autonomous Agent 系统，要求从对话工具转向可执行任务的智能体，覆盖 planning、acting、reflection、Tool/API 调用、长短期记忆、多轮推理、状态管理、多 Agent 协作、RAG 融合和 Eval。
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## Responsibilities
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- 构建具备任务执行能力的 Agent 应用系统。
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- 搭建 Tool/API、memory、reasoning、state 等核心能力。
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- 推动 Agent 在搜索、医疗、企业服务、数据分析和自动化办公等场景落地。
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## Extracted Signals
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- hard requirements: full-stack Agent engineering, tool/function calling, memory, state, eval。
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- preferred skills: 前沿方向跟踪，包括 Tool Learning、Self-Improvement、World Model、多模态 Agent。
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- hidden signals: 全栈岗位已经不只是 Web 工程，开始要求 Agent runtime 和评估能力。
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- business direction: 企业服务、数据分析、自动化办公。
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## Knowledge Links
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- concepts: workflow-agent, human-in-the-loop, eval
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- papers: Dialogue-SWEBench, SWE-EVO
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- experiments: workflow-agent prototype
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- projects:
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## Gaps for Us
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- 需要补一个 Agent runtime / state management 的工程专题。
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- 需要整理 Tool Learning 和 Self-Improvement 的论文清单。
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# Job: 百度 - 2027AIDU 大模型 Infra 工程师
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---
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type: job
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company: 百度
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role: 2027AIDU-大模型Infra工程师
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location: 北京市
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source_url: https://talent.baidu.com/jobs/list?projectType=3&recruitType=GRADUATE
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source_name: 百度校园招聘
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source_quality: official
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collected_at: 2026-07-08
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posted_at: 2026-05-12
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first_seen_at: 2026-07-08
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last_checked_at: 2026-07-08
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snapshot_type: new
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job_code: J99967
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previous_snapshot:
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status: active
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level: graduate
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team: AIDU项目
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business_area: model infrastructure
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employment_type: campus
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salary:
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skills:
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- inference
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- serving
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- distributed-training
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- gpu
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- model-compression
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- cloud
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topics:
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- llm-infra
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- inference
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models:
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-
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related_papers:
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-
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related_experiments:
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-
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related_projects:
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-
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relevance: medium
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---
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## Source Snapshot
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- source: 百度官方招聘页
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- collected_at: 2026-07-08
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- availability: 页面可访问，岗位列表显示发布日期 2026-05-12。
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## Raw JD Summary
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大模型 Infra 岗位关注 AI 异构计算加速引擎、底层算子、通信库、编译系统、分布式系统和训练/推理效率优化。
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## Extracted Signals
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- hard requirements: inference engine、通信库、算子、编译优化、分布式系统。
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- hidden signals: Agent 能力落地会被成本、延迟和推理效率约束；JD 需求里 infra 和 Agent 应用是并行出现的。
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- business direction: 大模型平台化和成本优化。
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## Knowledge Links
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- concepts: inference, latency, cost control
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- papers:
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- experiments: agent cost/latency evaluation
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- projects:
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## Gaps for Us
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- Agent 知识库需要增加成本、延迟和推理服务专题。
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# Job: 字节 Seed - 大模型 Agent 研究工程师
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---
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type: job
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company: 字节 Seed
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role: 大模型Agent研究工程师-Seed
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location: unknown
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source_url: https://jobs.bytedance.com/experienced/position/7628902314323806469/detail
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source_name: 字节跳动招聘
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source_quality: official
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collected_at: 2026-07-08
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posted_at:
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first_seen_at: 2026-07-08
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last_checked_at: 2026-07-08
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snapshot_type: new
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job_code: 7628902314323806469
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previous_snapshot:
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status: unknown
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level: experienced
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team: Seed
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business_area: agent research and engineering
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employment_type: full-time
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salary:
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skills:
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- agent
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- memory
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- context-engineering
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- planning
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- multi-agent
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- llm-application
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topics:
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- harness
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- memory
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- context-compression
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models:
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- Seed
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related_papers:
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- 2026-memory-agent-survey
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- 2026-evomembench
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related_experiments:
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-
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related_projects:
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-
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relevance: high
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---
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## Source Snapshot
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- source: 字节跳动官方招聘页；网页正文由前端渲染，当前采集依赖搜索摘要。
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- collected_at: 2026-07-08
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- availability: URL 可定位到官方岗位页，但需要后续用浏览器或 API 抽取完整 JD。
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## Raw JD Summary
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搜索摘要显示该岗位负责大模型 Agent 系统研究与工程落地，围绕复杂任务执行持续优化 Agent 效果，关键词包括 harness、memory、context compression、planning/execution、multi-agent。
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## Extracted Signals
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- hard requirements: agent harness, memory, context compression, planning/execution, multi-agent。
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- hidden signals: Seed 把 Agent 作为研究与工程落地结合的问题，而不是纯应用开发。
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- business direction: 复杂任务执行和通用 Agent 能力。
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## Knowledge Links
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- concepts: context engineering, agent harness, memory
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- papers: Memory survey, EvoMemBench
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- experiments: context compression comparison
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- projects:
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## Gaps for Us
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- 需要后续用可重复脚本抓取字节招聘 JS 数据或手动浏览器快照。
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# Job: DeepSeek - Agent 方向招聘组合
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---
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type: job
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company: DeepSeek
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role: Agent 方向招聘组合
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location: 北京市 / 杭州市
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source_url: https://hub.baai.ac.cn/view/53416
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source_name: 智源社区转载量子位
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source_quality: secondary
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collected_at: 2026-07-08
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posted_at: 2026-03-27
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first_seen_at: 2026-07-08
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last_checked_at: 2026-07-08
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snapshot_type: new
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job_code:
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previous_snapshot:
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status: unknown
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level: mixed
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team: Agent / data evaluation / infra / product
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business_area: search / creation / multimodal / personal assistant / workflow
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employment_type: full-time / internship
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salary:
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skills:
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- agent
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- rl
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- agent-evaluation
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- tool-use
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- function-calling
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- memory
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- multi-agent
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- coding-agent
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- data-quality
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- inference
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topics:
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- agent-productization
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- evaluation
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- agent-infra
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models:
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- DeepSeek
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related_papers:
|
||||
- 2025-vijayvargiya-openagentsafety
|
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- 2026-agentrx
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related_experiments:
|
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-
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related_projects:
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-
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relevance: high
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---
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## Source Snapshot
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- source: 智源社区转载量子位文章，文中参考 DeepSeek 招聘入口。
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- collected_at: 2026-07-08
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- availability: 二手来源可访问；官方招聘站需要后续逐条核验。
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## Raw JD Summary
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该资料显示 DeepSeek 在 2026-03 开放一批 Agent 相关岗位，覆盖算法研究、数据评测、基础设施、产品经理和全栈工程。岗位信号集中在 RL/RLAIF、Agent 评测数据、工具调用、多轮交互、长期记忆、MCP、内部 Agent 框架、评测平台和 Agent runtime。
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## Extracted Signals
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- hard requirements: Agent algorithm, data/eval, infra, product scenario discovery。
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- preferred skills: 重度使用 Claude Code、Cursor、Copilot 等 AI 编程工具。
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- hidden signals: DeepSeek 的 Agent 招聘从基础模型研究转向产品化、数据闭环和运行时基础设施。
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- business direction: 搜索、内容创作、多模态、个人助理、自动化工作流。
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## Knowledge Links
|
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|
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- concepts: coding-agent, agent evaluation, MCP, runtime isolation
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- papers: OpenAgentSafety, AgentRx
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- experiments: tool-use benchmark, eval data construction
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||||
- projects:
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## Gaps for Us
|
||||
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||||
- 需要后续从 DeepSeek 官方招聘站逐条拆解岗位。
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- 需要补 MCP、Agent runtime isolation、AI coding tool 使用能力的实践笔记。
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@@ -0,0 +1,72 @@
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# Job: 腾讯云 - AI Agent 测试工程师
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---
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type: job
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company: 腾讯
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role: 腾讯云-AI Agent测试工程师
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location: 深圳市
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source_url: https://careers.tencent.com/jobdesc.html?postId=2055555661177204736
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source_name: 腾讯招聘
|
||||
source_quality: official
|
||||
collected_at: 2026-07-08
|
||||
posted_at: 2026-06-03
|
||||
first_seen_at: 2026-07-08
|
||||
last_checked_at: 2026-07-08
|
||||
snapshot_type: new
|
||||
job_code: 2055555661177204736
|
||||
previous_snapshot:
|
||||
status: active
|
||||
level: experienced
|
||||
team: CSIG
|
||||
business_area: cloud agent testing
|
||||
employment_type: full-time
|
||||
salary:
|
||||
skills:
|
||||
- agent
|
||||
- agent-evaluation
|
||||
- observability
|
||||
- cloud
|
||||
- kubernetes
|
||||
- system-design
|
||||
topics:
|
||||
- agent-testing
|
||||
- harness-engineering
|
||||
models:
|
||||
-
|
||||
related_papers:
|
||||
- 2026-agentrx
|
||||
- 2025-vijayvargiya-openagentsafety
|
||||
related_experiments:
|
||||
-
|
||||
related_projects:
|
||||
-
|
||||
relevance: medium
|
||||
---
|
||||
|
||||
## Source Snapshot
|
||||
|
||||
- source: 腾讯官方招聘页；搜索结果显示岗位名、业务线、地点和更新时间。
|
||||
- collected_at: 2026-07-08
|
||||
- availability: 官方 URL 可定位，但正文需要后续浏览器或 API 抽取。
|
||||
- note: 非北京岗位，作为 Agent 测试/评估需求信号保留。
|
||||
|
||||
## Raw JD Summary
|
||||
|
||||
搜索摘要显示该岗位要求理解 LLM 原理与局限，具备大型项目的 AI Harness Engineering 或 Agent 落地工程实践，并熟悉云原生、分布式存储和云数据库等工程基础。
|
||||
|
||||
## Extracted Signals
|
||||
|
||||
- hard requirements: Agent 测试、harness engineering、云原生和分布式基础设施。
|
||||
- hidden signals: 腾讯云把 Agent 测试作为独立工程岗位，说明 Agent 评估和可靠性正在产品化。
|
||||
- business direction: 云上 Agent 服务、企业级测试和质量保障。
|
||||
|
||||
## Knowledge Links
|
||||
|
||||
- concepts: evaluation, observability, trace, harness
|
||||
- papers: AgentRx, OpenAgentSafety
|
||||
- experiments: failed trajectory debugging
|
||||
- projects:
|
||||
|
||||
## Gaps for Us
|
||||
|
||||
- 需要整理 Agent 测试工程和 harness engineering 的方法论。
|
||||
+33
-17
@@ -6,47 +6,63 @@ status: seed
|
||||
|
||||
## Current Snapshot
|
||||
|
||||
- date:
|
||||
- date: 2026-07-08
|
||||
- scope: 北京，大厂 Agent / LLM 相关岗位，滚动最近 6 个月
|
||||
- sample size:
|
||||
- source mix:
|
||||
- sample size: 6 starter items
|
||||
- source mix: official 5, secondary 1
|
||||
- caveat: 这是第一轮快速 sweep，不是完整市场统计；字节和腾讯部分页面需要后续用浏览器/API 补全正文。
|
||||
|
||||
## Requirement Changes
|
||||
|
||||
| Period | Signal | Companies | Evidence | Notes |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| _TBD_ | _TBD_ | _TBD_ | _TBD_ | _TBD_ |
|
||||
| 2026-03 to 2026-07 | Agent 从研究走向产品化和工程化 | DeepSeek、百度、字节、腾讯 | DeepSeek 招聘波、百度 AIDU、字节 Seed、腾讯 Agent 测试 | 需要继续补齐官方岗位快照 |
|
||||
| 2026-05 to 2026-07 | Eval / harness / testing 变成显性能力 | 百度、腾讯、DeepSeek | 百度 J99969/J99974、腾讯 AI Agent 测试、DeepSeek 评测岗位 | 评估不再只是研究指标，而是岗位职责 |
|
||||
| 2026-05 to 2026-07 | Memory / context compression 进入核心要求 | 百度、字节 | 百度智能体算法、字节 Seed Agent 研究 | 与论文 memory benchmark 趋势重合 |
|
||||
|
||||
## High-frequency Skills
|
||||
|
||||
| Skill | Count | Companies | Notes |
|
||||
| --- | --- | --- | --- |
|
||||
| _TBD_ | _TBD_ | _TBD_ | _TBD_ |
|
||||
| agent | 5 | 百度、字节、DeepSeek、腾讯 | 多数岗位直接围绕 Agent 设计、落地或测试 |
|
||||
| memory | 4 | 百度、字节、DeepSeek | 长期记忆、短期记忆、context compression 反复出现 |
|
||||
| multi-agent | 4 | 百度、DeepSeek、字节 | 百度明确多 Agent 协作，DeepSeek 和字节出现复杂任务执行信号 |
|
||||
| agent-evaluation | 4 | 百度、DeepSeek、腾讯 | 评测体系、harness、测试工程成为硬需求 |
|
||||
| planning / tool-use | 3 | 百度、DeepSeek | planning、acting、tool/API/function calling 是核心机制 |
|
||||
| rag | 2 | 百度 | RAG+Agent 融合是应用岗位重点 |
|
||||
|
||||
## Company Differences
|
||||
|
||||
| Company | Signals | Notes |
|
||||
| --- | --- | --- |
|
||||
| DeepSeek | _TBD_ | _TBD_ |
|
||||
| 腾讯 | _TBD_ | _TBD_ |
|
||||
| 字节 | _TBD_ | _TBD_ |
|
||||
| 阿里 | _TBD_ | _TBD_ |
|
||||
| 百度 | _TBD_ | _TBD_ |
|
||||
| DeepSeek | Agent 产品化、数据评测、基础设施、AI coding tool 使用 | 当前主要来自二手文章和官方招聘入口线索，需逐条核验 |
|
||||
| 腾讯 | Agent 测试、harness engineering、云原生基础设施 | 当前保留一个非北京强信号，后续需补北京岗位 |
|
||||
| 字节 | Seed 侧重 harness、memory、context compression、planning/execution | 官方岗位页 JS 渲染，需要 API/浏览器补全 |
|
||||
| 阿里 | Qwen 行业实践已出现 AgentWorld/Qwen Agent 信号 | JD 尚未入库 |
|
||||
| 百度 | 岗位描述最完整，明确 Agent algorithm、fullstack、infra 三层 | 官方页面可直接抽取，适合作为第一批标杆 |
|
||||
|
||||
## Role Patterns
|
||||
|
||||
- Agent engineer:
|
||||
- LLM application engineer:
|
||||
- AI infra engineer:
|
||||
- AI product manager:
|
||||
- AI research engineer:
|
||||
- Agent engineer: planning、tool use、memory、multi-agent、RAG、eval。
|
||||
- LLM application engineer: 从对话应用转向可执行任务系统，要求 state management 和 tool/API。
|
||||
- AI infra engineer: 推理、通信库、算子、分布式系统和成本优化支撑 Agent 落地。
|
||||
- AI product manager: DeepSeek 资料显示 Agent 产品经理需要懂场景、训练数据和评测体系。
|
||||
- AI research engineer: 字节 Seed 强调 harness、memory、context compression 和复杂任务执行。
|
||||
|
||||
## Knowledge Gaps
|
||||
|
||||
这些是 JD 里出现但知识库尚未充分覆盖的方向：
|
||||
|
||||
-
|
||||
- Agent harness engineering
|
||||
- Agent runtime isolation and scheduling
|
||||
- Memory and context compression evaluation
|
||||
- RAG+Agent product architecture
|
||||
- AI coding tool proficiency as an engineering skill
|
||||
- Agent test / failure diagnosis methodology
|
||||
|
||||
## Follow-up Collection Queue
|
||||
|
||||
-
|
||||
- 补齐 DeepSeek 官方招聘站 Agent 相关岗位详情。
|
||||
- 抓取字节招聘 JS 页面或 API，补全 Seed Agent 岗位正文。
|
||||
- 继续找腾讯北京 Agent / 大模型评测 / 元宝 / 混元相关岗位。
|
||||
- 开始阿里 Qwen、百度社招、美团/快手/京东 Agent 岗位 sweep。
|
||||
|
||||
Reference in New Issue
Block a user