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Open nowPosted 831 days ago

AI 可观测系统研发工程师

vrfi1sk8a0/referral187 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAI 可观测系统研发工程师vrfi1sk8a0/referral · 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 vrfi1sk8a0/referral's own form.

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. vrfi1sk8a0/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 831 days ago

vrfi1sk8a0/referral median: 1 days open

The posting

一、一句话定位 面向大模型训练、大模型推理与 Agent 运行时建设新一代可观测平台，连接硬件、网络、存储、模型框架、推理引擎和 Agent 执行轨迹，让复杂 AI 系统的异常看得见、定得准、恢复快、价值可衡量。

二、为什么这个岗位重要 大模型系统的问题往往横跨 GPU、网络、存储、容器、框架、推理引擎、模型行为和外部工具。我们希望建设统一的数据与诊断能力，回答三类核心问题： 训练为什么中断、变慢或损失有效算力； 推理为什么首 Token 变慢、吞吐下降、成本升高或质量退化； Agent 为什么任务失败、循环调用、链路异常或 Token 消耗失控。

三、你将负责什么 1. 建设统一的 AI 可观测平台 建设指标、日志、Trace、事件的采集、处理、存储、查询与可视化能力； 建立作业、请求、Agent 任务与节点、GPU、Pod、进程、Rank、模型、版本之间的关联； 治理高基数、高频事件和大规模时序数据，探索异常检测、告警降噪与根因分析能力。

2. 支撑大模型训练可观测 建设覆盖基础设施、通信与运行时、训练框架、模型与数据的分层观测体系； 关联 job → pod/process → rank → node → GPU UUID，诊断训练 Hang、慢 Rank、硬件、网络、存储和 Checkpoint 异常； 量化训练进度、有效算力及重试、等待损耗，推动问题处置和效果验证闭环。

3. 支撑大模型推理可观测 建设覆盖网关、路由、批处理、推理引擎、模型实例和 GPU 的请求级观测； 观测 TTFT、TPOT、排队、吞吐、KV Cache、Token 等指标，定位 Prefill、Decode、调度与资源争用问题； 关联模型版本、参数、上下文、输出质量和成本，支持性能与质量回归分析。

4. 支撑 Agent 运行时可观测 建设 Agent 任务到模型、工具、子 Agent、记忆和知识库访问的完整执行轨迹； 支持顺序、并行、循环和分支结构，量化任务成功率、工具成功率、重试及 Token 成本； 结合规则、模型评估、人工反馈和业务结果，诊断依赖故障、工作流异常及质量回归。

四、我们期望你具备 基本要求 1. 计算机或相关专业本科及以上学历，或具备同等工程能力； 2. 具备 3 年以上可观测、基础架构、分布式系统、云原生平台或后端平台研发经验；资深候选人需有大型生产系统的设计与落地经历； 3. 熟练掌握 Go、Java、C++、Rust、Python 中至少一门语言，具备扎实的并发编程和系统设计能力； 4. 熟悉 Metrics、Logs、Traces、Events 中至少两类数据的完整工程链路； 5. 熟悉 Linux、网络、容器、Kubernetes 和分布式系统，能够开展跨层问题分析； 6. 具备良好的抽象、落地和协作能力，愿意深入理解 AI 工作负载。 加分项 1. 有 Prometheus/Mimir、VictoriaMetrics、Thanos、OpenTelemetry、Jaeger/Tempo、Loki/ELK、ClickHouse、Grafana 等系统经验； 2. 有 GPU、RDMA、NCCL、分布式训练、Checkpoint 或训练稳定性经验； 3. 有 vLLM、TensorRT-LLM、SGLang、TGI、模型网关或容量管理经验； 4. 有 Agent Runtime、工作流引擎、LLM Trace 或评测平台经验； 5. 有 eBPF、AIOps、开源贡献或从 0 到 1 建设平台的经验。

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