小红书【新加坡】AI-Native Engineering Quality & Efficiency Engineer

任职要求

Minimum Qualifications
Bachelor’s degree or above in Computer Science, Software Engineering, or a related technical field.
Strong programming ability in at least one language such as Python, Go, Java, TypeScript, or C++.
Experience in quality engineering, test development, backend engineering, platform engineering, SRE, DevOps, or engineering productivity.
Solid understanding of software delivery lifecycle, automated testing, CI/CD, release management, defect tracking, and root cause analysis.
Hands-on experience with automation frameworks, internal tools, data analysis, or engineering platforms.
Strong problem-solving skills and ability to break down ambiguous business and engineering problems into scalable technical solutions.
Good communication skills and ability to collaborate with product, eng…

工作职责

About the Role
We are looking for an AI-Native Engineering Quality & Efficiency Engineer to join our International Engineering team in Singapore. This role sits at the intersection of product engineering, quality engineering, AI tooling, and global compliance. You will build next-generation engineering productivity and quality platforms powered by LLMs and automation, supporting the full software delivery lifecycle for international products.
You will work closely with product, engineering, QA, Trust & Safety, compliance, data, and infrastructure teams to improve delivery quality, release efficiency, risk detection, and engineering experience across global markets.
Responsibilities
Own and improve the quality assurance lifecycle for international products, including functional quality, performance, compatibility, usability, release quality, and post-launch stability.
Design and implement AI-native engineering tools across requirement understanding, code generation, test generation, defect diagnosis, release validation, and incident analysis.
Build and maintain automated testing frameworks, quality gates, CI/CD quality checks, and engineering productivity platforms to improve delivery speed and quality coverage.
Apply LLMs, agents, RAG, prompt engineering, and workflow automation to real-world software engineering scenarios, turning repetitive engineering work into scalable intelligent systems.
Identify quality risks and engineering bottlenecks through data analysis, defect patterns, release metrics, incident reviews, and developer feedback.
Partner with Trust & Safety, Legal, Policy, and Compliance teams to ensure international product features meet local regulatory, content safety, data governance, and platform policy requirements.
Develop evaluation frameworks for AI-generated outputs, including test cases, code suggestions, defect analysis, policy enforcement workflows, and operational recommendations.
Drive root cause analysis for defects, production incidents, quality regressions, and compliance-related issues, and convert findings into reusable tools, standards, and best practices.
Collaborate with globally distributed teams to support international launches, regional adaptations, and continuous quality improvement.