位于北京市, c22的 20 个优质infrastructure engineer职位（正在招聘！）

Lenovo is a US$83 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub.

Description and Requirements

Responsibilities
1. Core Architecture & Delivery: Lead backend architecture design, core development, and delivery for AI & Agent projects. On-Device Integration & Optimization: Collaborate with core teams (e.g., Memory & Knowledge, Orchestration, Agent Runtime) to integrate AI modules (e.g., inference, Agent workflow, RAG, memory/context, Agent orchestration) on device. Ensure high-availability packaging and ultra-low-resource optimization.
2. Cross-Platform & Performance: Build cross-platform (Windows/Android) AI SDKs/apps, with a focus on memory, power, and latency optimization for mobile.
3. Engineering Efficiency: Write technical docs and API specs. Build and maintain automated testing and CI/CD pipelines to ensure quality and efficiency in cross-platform delivery.
4. Tech Leadership: Stay current with AI advancements. Mentor junior/mid-level engineers.

Qualifications

Education & Language Background

Education: Bachelor’s or higher in CS, AI, Software Engineering, or related field. Good English for reading docs/papers and global communication.
Experience: 5-8+ years in software development, with proven 0-to-1 delivery of complex projects.

Core Technical Skills

Deep Go Mastery: Master Go core syntax and features, with deep understanding of Goroutine scheduling (G-M-P), Channel communication, GC principles & tuning, and memory allocation (TCMalloc & on-device leak prevention).
Go + AI/Agent Experience: Hands-on experience building/integrating LLM backends, Agent Runtimes, or RAG systems with Go. Familiar with (or able to quickly ramp up on) Go AI ecosystem (e.g., LangChainGo), skilled in LLM API integration, and able to package locally deployed models as services.
Performance & CGO: Proficient with pprof for high-concurrency/low-latency optimization. Experienced with CGO to resolve Go/C++ lib performance bottlenecks (e.g., llama.cpp).
AI Domain Knowledge: Familiar with LLM app development, Prompt Engineering, Agent paradigms (e.g., ReAct, Plan-and-Solve), RAG workflows, and on-device DBs.

Additional Preferred Skills

Java: Solid Java background to enable smooth architecture, code, and microservice integration with existing Java teams.
Python: Strong ability to read Python code (e.g., LangChain, LlamaIndex) and refactor core logic into Go.
Rust (Plus): Highly valued for on-device optimization, safe memory management, and cross-platform low-level interaction (e.g., C-FFI).

Nice-to-Have

On-Device/Extreme Optimization (Strong Plus): Experience with on-device (Mobile/PC) background apps, daemons, or cross-platform SDKs. Experience in extreme memory optimization (OOM protection, defragmentation) and CPU/GPU inference efficiency. Familiar with on-device inference engines (e.g., llama.cpp, ONNX Runtime, CoreML, ExecuTorch).
High-quality contributions to Go/Rust/AI open-source projects on GitHub.
Cloud-native familiarity with Server-side AI or large-scale distributed systems experience. Knowledge of Kubernetes, gRPC, service mesh, and able to adapt to Enterprise AI expansion.
Good product sense (AI-friendly design) and HCI understanding for AI products.

Additional Locations:

China - Beijing - 北京（Beijing）

China

China - Beijing

China - Beijing - 北京（Beijing）

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