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Open nowPosted 101 days ago

Super Sparks-校招-智能体原生OS研究员/工程师

nio/campus870 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowSuper Sparks-校招-智能体原生OS研究员/工程师nio/campus · Shanghai, Chinese Mainland

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  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 101 days ago

The posting

职位介绍 (Position Overview) 我们正在定义和构建下一代智能体原生操作系统（Agent Native OS），致力于将操作系统的核心服务对象从“传统应用”向“AI 智能体”转移。由于该前沿领域横跨底层系统架构（OS Internals）与人工智能基础设施（AI Infra）两大硬核学科。 我们正在寻找在 操作系统底层 或 AI 基础设施 (MLSys) 任意一端具备深厚学术积累的顶尖博士人才。您将在我们拥有互补背景的跨学科团队中，主导前沿技术的探索与原型验证，攻坚大模型运行时调度、原生 AI 内存管理以及系统级安全隔离等底层基座难题，逐步跨界成长为下一代 Agent OS 的核心架构领军者。 岗位职责 (Responsibilities) 下一代智能体系统基座验证： 针对多智能体高并发与强隔离的需求，在系统架构（如基于 seL4 等微内核及其 User-mode VMM 机制）、软硬件协同的异构互联、或智能体可信执行环境（TEE）等关键路径上，完成核心模块的概念验证（PoC）与架构设计。 跨界融合创新 (OS-Runtime Co-design)： 如果您是 OS 专家： 带领团队探索分级内存（Tiered Memory / CXL）在 OS 层的管理机制，优化底层文件系统与内存页调度，为 AI 运行时提供极速的 KV Cache 卸载（Offloading / Swapping） 支持，从系统底层突破长上下文（Long Context）带来的内存墙瓶颈。 如果您是 AI Infra 专家： 打破传统推理框架的局限，探索算子调度、显存碎片管理与底层操作系统的深度协同；研究 PageAttention 等极致内存机制在操作系统原语级别的更优实现。 面向 Agent 语义的 IPC 与安全沙盒： 研发专为多智能体协作设计的极低延迟跨进程通信机制（如张量传递 Zero-copy Tensor Passing）；并结合 User-mode VMM、WASM 或 TEE 技术，为 Agent 的不受信工具调用（Tool Calling / Code Interpreter）构建毫秒级启动、基于能力（Capability-based）的强隔离执行沙盒。 学术转化与技术攻坚： 紧跟 MLSys、OSDI、SOSP 等顶级学术会议的最新研究进展，将前沿的软硬协同理论转化为可量产、高可靠的智能体底层软件基础设施。

基本要求： 教育背景： 拥有计算机科学、计算机工程或相关领域的博士学位（Ph.D.）。 研究与工程能力： 具备深厚的学术研究基础与独立探索前沿技术的能力；同时具备强大的问题解决能力与扎实的系统级编程基础，精通 C/C++ 或 Rust。 核心技术栈要求（以下两个方向满足其一即可）： 方向 A：深耕底层操作系统 (OS & Systems Track) 主攻专长： 对操作系统内核、内存管理（特别是 Tiered Memory, CXL, 虚拟内存子系统）、微内核（如 seL4, Zircon）或底层虚拟化（如 User-mode VMM 设计与实现）有深刻的理论基础与修改底层源码的丰富实践经验。熟悉现代系统安全隔离机制。 跨界潜力： 对人工智能底层逻辑充满好奇心；愿意在入职后深入学习大语言模型（LLM）的工作原理及其对算力/内存的系统级需求特征。 方向 B：深耕 AI 基础设施 (AI Infra & MLSys Track) 主攻专长： 深入理解大语言模型（LLM）的底层显存消耗与调度机制；熟悉主流推理加速框架（如 vLLM, TensorRT, Triton），并在异构计算、分布式张量并行（TP）或分布式 KV Cache 调度方面有深度的系统级优化（非纯算法）经验。 跨界潜力： 具备扎实的计算机系统结构认知；愿意在入职后打破框架层的局限，向下深入学习微内核机制、底层内存分配与硬件驱动运行逻辑。 加分项 (Preferred Qualifications) 顶会发表： 在计算机系统、体系结构或 AI 基础设施领域的顶级学术会议（如 SOSP, OSDI, ASPLOS, MLSys, EuroSys, 等）上有高水平论文发表记录。 开源贡献： 在顶级系统开源项目（如 Linux Kernel, seL4, 知名 Rust 底层项目）或 AI 基础设施开源项目（如 vLLM, Llama.cpp, Ray）中有核心代码贡献。 底层优化： 熟悉跨架构开发与指令集级优化（如 x86, Arm, RISC-V）。 工业界经验： 在校期间有顶尖工业界实验室的实习经历，具备将学术界原型转化为高可用工程代码的成功经验。

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