AI软件工程师 - 代码智能与质量验证

The Agentic Ops US team focuses on two major areas: Code Graph and Quality Validation.

The code graph serves as the data foundation for validation, encompassing static relationships (function calls, experiment/instrumentation dependencies) and dynamic execution paths (reconstructed from traces).

Quality validation covers static rule checking (mining soft and logical constraints) and dynamic issue detection, reproduction, and fixing.

We aim to improve code reliability and R&D efficiency through systematic, data-driven approaches.

- Design, develop, and optimize Code Graph capabilities, including static relationship extraction, dynamic trace processing, execution path reconstruction, and graph data modeling.

- Contribute to the architecture and technical design of core systems, with a system-level view of module boundaries, dependencies, scalability, and maintainability, and drive technical solutions through implementation.

- Design and develop static validation capabilities based on the code graph, including mining and validating logical and soft constraints, while continuously improving rule accuracy and coverage.

- Build dynamic validation capabilities for production issue detection, reproduction, root cause localization, and assisted fixing, and develop reusable solutions for common problem patterns.

- Conduct in-depth analysis of complex production quality issues by leveraging code, traces, and other runtime signals, identify root causes, extract generalized patterns, and drive automated validation coverage.

- Develop a deep understanding of code organization, module dependencies, build pipelines, and runtime behavior in large-scale client applications, and explore how Code Intelligence can improve the understandability and quality of complex codebases.

- Explore and introduce techniques in code analysis, program slicing, anomaly detection, LLMs / Agents, and related areas, and apply them to real-world engineering problems.

- Bachelor's degree or above in Computer Science, Software Engineering, Artificial Intelligence, or a related field, with 3+ years of professional software development experience.

- Strong programming fundamentals and proficiency in at least one mainstream programming language, such as Java / Kotlin / Go / Python / C++, with solid coding practices and engineering skills.

- Strong system design and software architecture skills, with the ability to decompose complex systems, define clear module boundaries, interfaces, and dependencies, and make sound trade-offs across maintainability, scalability, reliability, and performance.

- Hands-on experience with large-scale or complex engineering projects, with the ability to independently analyze problems, design technical solutions, implement core components, and drive solutions to production.

- Strong foundation in data structures and algorithms, with strong problem abstraction and analytical skills and the ability to quickly understand complex codebases and execution flows.

- Strong investigative mindset and curiosity about unfamiliar problems, with the ability to learn deeply from technical documentation, source code, papers, and other resources and translate insights into practical engineering solutions.

- Strong learning agility, with the ability to quickly ramp up on new technical domains, codebases, and complex systems.

- Android development experience is preferred, especially experience with Android Framework, application architecture, modularization, build systems, performance optimization, or large-scale Android applications.

- Experience with static analysis (e.g., AST, CFG, Call Graph, Data Flow / Pointer Analysis), program analysis, or compiler development.

- Experience with dynamic tracing, observability, or production diagnostics, such as eBPF, OpenTelemetry, or distributed tracing.

- Experience with Code Intelligence, Developer Infrastructure, automated testing, quality platforms, or Root Cause Analysis.

- Experience building large-scale distributed systems, graph processing systems, search/indexing infrastructure, or related platforms.

- Experience with LLMs, AI Coding Agents, Agentic Workflows, or AI for Software Engineering.

- Publications in academic conferences or journals related to code analysis, software engineering, or system reliability, or significant contributions to major open-source projects.