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Salary
≈ $22k – $57k per year (Estimated)
Location
In office (Beijing)
Seniority
Architect

Confirmed on the employer's own hiring board on Oct 21, 2026. First seen by Alion on Jun 17, 2026. Baidu scores C on the Alion truth index.

Overview
Company
Impact
Profile match
Baidu is a Chinese technology company founded in 2000 by Robin Li and Eric Xu that operates the dominant search engine in mainland China. Around search it has built a large advertising business, the Baidu Maps and Baijiahao content platforms, and an AI Cloud division selling compute, data and model services to enterprises and government. The company was one of the first Chinese firms to commit heavily to artificial intelligence, developing the ERNIE family of large language models and the Apollo autonomous driving stack that powers its Apollo Go robotaxi fleet in several Chinese cities.

工作职责 / Responsibilities

-主导 PostgreSQL 内核级功能设计与优化

-针对 AI/ML 工作负载(如向量检索、批量推理数据加载、实时特征存储)优化 PG 查询引擎与存储层

-支持扩展如 pgvector、lantern、pg_embedding 等向量插件的深度集成与性能调优

-构建下一代云原生 PG 架构(对标 Neon、Supabase)

-设计并实现 计算与存储分离架构,支持秒级弹性扩缩容、按需计费、多租户隔离

-实现 数据库分支(Branching)能力,支持开发/测试/CI 场景下的瞬时克隆与版本管理

-构建基于对象存储(如 S3)或分布式日志(如 WAL-on-S3)的持久化层,保障低成本与高可用

-打造 AI-Ready 数据库服务

-为 LLM 应用提供原生支持:高效存储上下文、对话历史、向量索引与元数据联合查询

-与 AI 平台(如 ModelScope、PAI、SageMaker)深度集成,提供“数据库即特征仓库”能力

-探索在数据库内运行轻量级推理(In-Database ML)或 UDF 扩展(如 Python/Rust UDF)

-高可用、智能运维与自治增强(延续并升级)

-结合 AIOps 实现自动索引推荐、参数调优、异常根因分析

-支持 Serverless 模式下的冷启动优化、连接池代理、请求级资源隔离

-技术前瞻与生态引领

-跟踪 Neon、Tembo、Supabase、FerretDB 等新兴 PG 生态的技术路线

-推动开源协同,探索将内部创新回馈社区(如开源分支管理、WAL 分离组件等)

职责要求 / Requirements

-必备条件(Must-have)

-深厚的 PostgreSQL 内核功底

-熟悉 PG 源码(v12+),有内核模块开发经验(如自定义 AM、Hook、WAL 重放逻辑)

-理解 MVCC、WAL、复制槽、逻辑解码等机制,能针对云环境重构关键路径

-云原生数据库实战经验

-有存算分离、共享存储、无服务器(Serverless)数据库架构设计或开发经验

-熟悉 Neon、Aurora、PolarDB、CockroachDB、Vitess 等系统者优先

-掌握 Kubernetes Operator、Sidecar 模式、Service Mesh 在数据库控制面的应用

-AI/向量场景理解与工程落地能力

-了解向量数据库基本原理(HNSW、IVFFlat)、PG 向量扩展生态

-有为 AI 应用构建数据管道、特征存储或实时检索系统的经验

-系统工程与性能调优能力

-精通 C/Rust,熟悉 Linux I/O 栈、eBPF、io_uring、RDMA 等底层技术

-能在百万 QPS 或 PB 级数据下定位瓶颈并设计可扩展方案

-加分项(Nice-to-have)

-深入研究过 Neon 的架构(Pageserver + Safekeeper + Compute 分离模型)并有复现或改进经验

-参与过开源向量数据库(如 Weaviate、Qdrant)或 PG 扩展项目

-熟悉数据库与 LangChain/LlamaIndex 等 AI 框架的集成模式

-有 Serverless 平台(如 AWS Lambda、阿里云 FC)与数据库协同调度经验

-在 GitHub 或社区发布过 PG 相关工具、Benchmark 或 RFC

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