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Open nowPosted 4 hours ago

AI Platform 工程师

kaonai/index31 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAI Platform 工程师kaonai/index · 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 kaonai/index'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.

13.1% of postings close within 7 days. Measured by our own scanner across the market. kaonai/index postings stay open a median of 5 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.8%30 days
  6. 59.0%60 days
This job: posted 4 hours ago

kaonai/index median: 5 days open

The posting

我们希望您是一名真正的 AI 工程师：热爱 AI，持续、深入地使用 AI，并愿意用 AI 重新思考产品与工程的实现方式。您将参与 AI Platform 核心能力从探索、验证到规模化落地的全过程，让更先进的模型能力更快、更稳定地服务全球用户。

1. 参与 AI Platform 核心方向的规划、设计与实现，对从想法验证到生产落地的完整闭环负责。 2. 探索大模型与 Agent 在真实产品中的新形态与新应用，把不确定的想法快速转化为可验证、可迭代的产品能力。 3. 将模型的最新进展转化为稳定、可复用的平台能力，提升各团队开展实验、评测与交付 AI 产品的效率。 4. 以 AI Native 的方式工作，持续提升需求理解、方案设计、开发验证与问题解决的效率和质量。 5. 与算法、数据、产品、客户端和基础设施团队紧密协作，以用户价值为导向，推动复杂问题高质量落地。 6. 对系统的长期质量、稳定性、成本效率与可演进性负责，让平台持续支撑业务增长。

1. 本科及以上学历，计算机或相关专业，5 年及以上后端开发经验。 2. 熟练掌握 Go 或 Node.js；具备 Python、Java 或 Rust 等语言的使用经验，或能够根据实际需要快速学习。 3. 具备生产级后端或平台系统经验，设计和维护过复杂的高并发、高可用系统，能够妥善处理流式响应、长耗时请求、依赖故障、超时重试与兼容性问题。 4. 具备全局系统架构设计能力，能够围绕高并发、高可用目标完成服务拆分、流量治理、容灾降级、数据一致性与容量规划，并设计可持续演进的系统架构。 5. 熟悉 PostgreSQL、Redis、Kafka、Kubernetes 等常见基础设施；能够设计清晰的服务边界、数据模型、状态机、API 与 SDK，并理解幂等、状态恢复、容量治理和可观测性。 6. 具备良好的数据与实验思维，理解事件采集、指标口径、样本偏差、显著性、置信区间、历史回放和因果归因；不要求是统计学家，但需要尊重证据与数据边界。 7. 热爱 AI，并在日常工作中持续、深入地使用 AI；能够把大模型和 Agent 作为工程工具，对 Prompt、Context / Token、流式生成、超时重试、Provider 差异与 Tool Calling 等核心链路和能力边界有清晰判断。 8. 具备良好的系统抽象与工程判断能力，能够在快速交付和长期可维护性之间做出合理取舍。 9. 具备较强的 Ownership、沟通与文档能力，愿意直面线上问题，能够推动跨团队协作与问题闭环。

具备以下任一方向的深入经验者优先 1. 建设过 A/B 实验、Feature Flag、灰度发布、指标平台或因果分析系统。 2. 熟悉 Flink、Paimon、Kafka Streams 或其他流式计算与湖仓技术，并处理过乱序、迟到、去重、Checkpoint 和 Replay。 3. 参与建设过高并发实时聊天、LLM Gateway、Agent 平台、模型评测或多模态生成系统。 4. 熟悉实验统计方法，例如序贯检验、CUPED、SRM、功效分析、Bayesian 方法或多重比较校正。 5. 具备 SDK、协议、编译内核或跨语言基础库经验，熟悉 Go / Rust / Python / Java 之间的工程协作。 6. 具备复杂生产系统的性能优化、稳定性治理或事故处理经验。

我们不要求您从一开始就精通上述所有技术。相比关键词数量，我们更看重系统能力、学习速度、结果意识，以及把复杂问题拆解并做成可靠产品的能力。您可能参与：

1. 探索 Agent 与人协作的新范式，把模糊想法转化为真正可用、能够持续演进的 AI 产品能力。 2. 将模型的最新进展转化为稳定、易用的产品能力，让全球用户与开发者直接受益。 3. 打造支撑 AI 产品的高并发、高可用基础设施，在交付速度、系统质量、成本效率与可观测性之间做出一流的工程取舍。 4. 从 0 到 1 发起并主导新的技术或产品方向，对方案、落地、推广与最终结果负责。 5. 建设面向模型与 Agent 的运行时、实验和评测体系，让模型潜力稳定转化为产品的基础能力。 6. 将关键技术沉淀为开放标准、SDK 或开源项目，与全球开发者和客户跨越时区、语言和组织边界，共同解决高价值问题。 7. 与优秀的人共同成长，参与关键人才引入、工程实践传承和团队能力建设，持续提升团队的人才密度与组织效率。

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