Software Engineer, Quality Platform
On-siteSan Francisco, California, United States
Job Summary
Build AI agents for the testing lifecycle, from test case generation to automation and coverage maintenance. Develop end-to-end systems in TypeScript, Swift, Kotlin, Java, and Python that integrate contextual insights into CI/CD pipelines and developer workflows. Design scalable AI-driven systems for agentic E2E validation, exploratory testing, and business-critical quality gates. Collaborate cross-functionally with Mobile, Infrastructure, and Product Engineering teams to embed AI into real-world development environments. Explore prompt engineering and RAG to improve system adaptability while mentoring colleagues on applying AI in engineering systems.
Required Qualifications
- 4+ years of software engineering experience in high-scale environments
- ideally building platforms, infra, or developer/quality tooling
- exposure to or strong interest in AI/ML or LLM-based systems
- Experience with testing frameworks
- Experience with CI/CD pipelines
- Experience with developer experience tooling
- Experience with quality engineering platforms
- Hands-on experience building applications using LLMs (prompting, APIs, RAG, evaluation, or similar)
- Experience building both client and server-side systems
- Experience working on modern web/mobile stacks (Swift, Kotlin)
- Experience with supporting backend services (Java, Python)
- Understanding of distributed systems
- Understanding of CI/CD workflows
- Understanding of large-scale software architectures
- Ability to navigate ambiguity and design practical, scalable solutions
- Strong ability to collaborate across teams and explain complex concepts clearly
Desired Qualifications
- building AI Agents for the Testing Lifecycle
- Move beyond traditional automation by building systems that continuously adapt test coverage based on code changes and system behavior
- Identify bottlenecks in testing and CI/CD workflows, and solve them through intelligent automation that reduces manual effort and accelerates feedback loops
- Embed AI capabilities into real development environments, enabling both developers and quality engineers to receive contextual, actionable insights during development and testing
- Develop AI-assisted capabilities such as context-aware agentic E2E validation on pull requests
- agentic execution of business-critical flows as quality gates
- agentic surface discovery and exploratory testing
- systems for test case generation
- coverage maintenance
- Participate in the design of scalable AI-driven systems operating within large-scale engineering environments
- Work closely with Mobile, Infrastructure, Product Engineering, and Quality Engineering teams to integrate AI into real-world development workflows
- Explore approaches such as prompt engineering and RAG (Retrieval-Augmented Generation) to improve system effectiveness and adaptability
- Provide technical guidance and help raise the bar for applying AI in engineering systems across the team
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