Platform Development & System Software Engineer平台开发与系统软件工程师

Liu Tianyi

Focused on system software and engineering infrastructure — abstracting complex, repetitive system-level work like driver builds, firmware tests, crash analysis, and performance tuning into reusable, traceable, scalable automation tool-chains.专注于系统软件与工程基础设施建设，擅长将驱动构建、固件测试、崩溃分析、性能调优等复杂且重复的系统级工作，抽象为可复用、可追踪、可规模化运行的自动化工具链。

Drawn to the engineering patterns behind the data, and continually exploring how AI Agents and knowledge engineering land in system diagnostics — freeing teams from heavy manual analysis to locate issues faster, ship software more reliably, and drive innovation more efficiently.关注数据背后的工程规律，也持续探索 AI Agent 与知识工程在系统诊断中的落地，让团队从繁重的人工分析中解放出来，更快定位问题、更稳定交付软件、更高效地推动创新落地。

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About关于

A bit about me关于我

I'm Liu Tianyi — a system-software and engineering-infrastructure engineer based in Shanghai.我是刘天义，一名常驻上海的系统软件与工程基础设施工程师。

My work has long centered on drivers, firmware, test infrastructure, and system-level problem diagnosis. At Lenovo I own the drivers and platform tooling around the Windows core stack — Display, Graphics, and Power — backing validation, delivery, and root-causing across the Yoga and ThinkBook lines. Before that, at Quanta, I built the EFI / UEFI firmware test infrastructure for Apple MacBook and worked directly with Apple's engineering team on firmware and hardware-adaptation issues.我的工作长期围绕驱动、固件、测试基础设施与系统级问题诊断展开。在联想，我负责 Windows Display、Graphics、Power 等核心栈相关的驱动与平台工具，支撑 Yoga 与 ThinkBook 产品线的验证、交付和问题定位；此前在广达，我为 Apple MacBook 搭建 EFI / UEFI 固件测试基础设施，并直接与 Apple 工程团队协作解决固件与硬件适配问题。

I like breaking complex problems into clear engineering models, and I habitually turn slow, repetitive, experience-dependent processes into automated systems. Driver builds, firmware tests, WHQL certification, Windows Update release, BSOD crash analysis, trace-data processing, performance and power tuning — this seemingly scattered work can all be abstracted into stable, reusable, traceable engineering infrastructure.我喜欢把复杂问题拆成清晰的工程模型，也习惯把缓慢、重复、依赖人工经验的流程改造成自动化系统。驱动构建、固件测试、WHQL 认证、Windows Update 发布、BSOD 崩溃分析、Trace 数据处理、性能功耗调优——这些看似分散的工作，本质上都可以被抽象为稳定、可复用、可追踪的工程基础设施。

I move comfortably across driver validation, Windows services, automated testing, system diagnostics, performance analysis, and cross-vendor collaboration; I also enjoy using full-stack web, Docker, CI/CD, and all kinds of internal tools to turn ideas into working systems fast. C#, C++, Python, Go, and Node.js are my everyday languages — and AI Agents, knowledge engineering, and tool-calling are becoming my new way to scale engineering efficiency.我可以在驱动验证、Windows 服务、自动化测试、系统诊断、性能分析与跨厂商协作之间切换，也乐于用全栈 Web、Docker、CI/CD 和各种内部工具，把想法快速落成可用的系统。C#、C++、Python、Go 与 Node.js 是我常用的语言，而 AI Agent、知识工程和工具调用，则正在成为我扩展工程效率的新方式。

I believe great engineering systems don't just get the job done — they cut repetitive labor, accumulate expertise, and help a team understand problems, validate hypotheses, and deliver results faster.我相信优秀的工程系统不只是完成任务，更应该减少重复劳动、沉淀专业经验，并让团队更快地理解问题、验证假设和交付结果。

Work作品

Projects项目

Platforms and engineering infrastructure I've built — from automated crash diagnosis and driver delivery pipelines to trace-based analysis tools and internal systems that turn manual engineering work into repeatable, scalable workflows.我构建过的平台与工程基础设施——从自动化崩溃诊断、驱动交付流水线，到基于 Trace 的分析工具与内部工程系统，将依赖人工经验的流程沉淀为可复用、可规模化运行的工作流。

Skills技能

Skillset技能栈

Core systems, debugging, automation, and infrastructure tools I use to build reliable engineering workflows.我用于构建可靠工程工作流的系统软件、调试、自动化与基础设施技术栈。

C / C++ Systems-level tooling, driver-adjacent infrastructure, performance-sensitive utilities, and Windows core-stack work.系统级工具、驱动相关基础设施、性能敏感型工具，以及 Windows 核心栈开发。
Python Automation, trace/log processing, test tooling, and the LLM + WinDbg analysis engine behind diagnostic platforms.自动化、Trace/日志处理、测试工具，以及诊断平台背后的 LLM + WinDbg 分析引擎。
C# / .NET Internal platforms, services, and desktop tooling.内部平台、服务与桌面工具。
Go Lightweight backend services, internal APIs, and deployable CLI utilities for engineering workflows.轻量后端服务、内部 API，以及面向工程工作流的可部署 CLI 工具。
AI Agents Skills + Loop task-driven agents, knowledge retrieval, tool-calling workflows, and AI-assisted root-cause analysis.Skills + Loop 任务驱动 Agent、知识检索、工具调用工作流，以及 AI 辅助的根因分析。
CI/CD Build, test, and release pipelines for drivers, firmware, and platforms.驱动、固件与平台的构建、测试、发布流水线。
WinDbg / ETW / ETL Driver-stack root-cause analysis: crash dump inspection, symbol-level debugging, and trace-based diagnostics.驱动栈根因分析：崩溃转储检查、符号级调试，以及基于 Trace 的诊断。
Perf / Power PowerLimit tuning, benchmark analysis, performance-regression investigation, and MSR-based platform counters.PowerLimit 调优、Benchmark 分析、性能回退排查，以及基于 MSR 的平台级 Counter。
WHQL / HLK Driver certification, multi-arch testing, signing, Windows Update.驱动认证、多架构测试、签名与 Windows Update。
Docker / Hyper-V Reproducible build, test, and lab environments.可复现的构建、测试与实验环境。

Experience经历

Where I've worked工作经历

Lenovo

Nov 2022 — Present

Software Engineer — Test Infrastructure / System Software软件工程师 — 测试基础设施 / 系统软件

  • Core engineer for Windows platform and driver infrastructure across Lenovo consumer products — covering Display, Graphics, and Power modules for Yoga and ThinkBook validation, delivery, and mass-production support.联想消费级产品线 Windows 平台与驱动基础设施核心工程师，覆盖 Yoga 与 ThinkBook 的 Display、Graphics、Power 模块验证、交付与量产支持。
  • Drive system performance and power tuning across these platforms, including dynamic PowerLimit strategies for real-world scenarios, benchmark and competitive analysis, and stutter/jank root-cause investigation using ETW/ETL traces and MSR-based performance counters.负责平台级性能与功耗调优，包括面向真实使用场景的动态 PowerLimit 策略、Benchmark 与竞品分析，以及基于 ETW/ETL Trace 和 MSR Performance Counter 的卡顿 / 性能异常根因定位。
  • Designed and shipped CI/CD and automated-test infrastructure covering driver build, validation, certification, and release, reducing multi-hour manual delivery flows to minutes.设计并落地覆盖驱动构建、验证、认证与发布的 CI/CD 和自动化测试基础设施，将原本数小时的人工交付流程压缩至分钟级。
  • Own WHQL certification and release for 10+ drivers: HLK setup, multi-arch loop testing, signing, and Windows Update — 100% compliant delivery.全面负责 10+ 驱动的 WHQL 认证与发布：HLK 搭建、多架构循环测试、签名与 Windows Update——100% 合规交付。
  • Collaborate closely with Intel and Microsoft engineering teams to root-cause driver, OS, platform, and compatibility issues, and to deliver system-level features that improve reliability, performance, and end-user experience.与 Intel、Microsoft 工程团队紧密协作，联合定位驱动、OS、平台与兼容性问题，并推动提升可靠性、性能和终端体验的系统级功能落地。
  • Built a 0-to-1 BSOD intelligent-diagnosis platform using a Skills + Loop task-driven agent architecture, integrating dump parsing, symbol resolution, issue data, and knowledge retrieval to infer and classify crash root causes.从 0 到 1 构建基于 Skills + Loop 任务驱动架构的 BSOD 智能诊断平台，整合 Dump 解析、Symbol 还原、issue 数据与知识检索，实现崩溃根因推断与自动分类。
  • Reduced average localization time by 80%, bringing AI-assisted debugging into test and analysis workflows and shifting the team from manual test execution toward platform-driven engineering.将平均定位周期缩短 80%，把 AI 辅助调试引入测试与分析流程，推动团队从人工执行测试转向平台化工程运营。

Quanta

Oct 2020 — Feb 2022

Firmware Test Infrastructure Engineer — EFI / UEFI & System Debugging固件测试基础设施工程师 — EFI / UEFI 与系统调试

  • Owned EFI/UEFI firmware test infrastructure and QA for Apple MacBook — cross-site, cross-region test flows and multiple Dry Run cycles before mass production.负责 Apple MacBook 的 EFI/UEFI 固件测试基础设施与质量保障——跨站点、跨地域测试流程，量产前多轮 Dry Run。
  • Root-caused complex firmware and system issues from logs and core dumps; built layered validation and regression plans to speed R&D fixes.基于日志与 Core Dump 对复杂固件与系统问题做根因定位；制定分层验证与回归方案，加速研发修复。
  • Served as the EFI test technical interface across hardware, BIOS, and key-component teams, working directly with Apple engineering on version iteration, issue triage, and quality evaluation.担任 EFI 测试技术接口人，协同硬件、BIOS 与关键部件团队，并直接对接 Apple 工程团队进行版本迭代、问题分流与质量评估。
  • Automated the test flow with Python tooling, cutting manual effort and human error.用 Python 工具自动化测试流程，降低人工投入与人为误差。

Open Source开源

GitHub ActivityGitHub 活跃度

A year of commits, reviews, and late-night refactors.一年的提交、评审，和深夜重构。

1,819 contributions in the last year 过去一年共 1,819 次贡献

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