Waters Corporation is a global leader in analytical instruments and technologies, and they are seeking a summer intern for their Digital Commerce team. This internship provides an opportunity to gain real-world experience in quality engineering, focusing on API and UI automation, testing lifecycle management, and exploring AI-driven solutions.
Responsibilities
- Architect Trust with Karate: Design and execute sophisticated API and UI automation scripts using the Karate framework, ensuring our commerce engines remain robust even as underlying code is accelerated by AI
- Orchestrate Quality with Tricentis qTest: Manage the end-to-end testing lifecycle within Tricentis qTest, integrating automated results with manual insights to provide a single source of truth for platform health
- Pioneer the Agentic SDLC: Work directly with our engineering team to explore how AI agents can autonomously trigger tests, analyze failures, and suggest fixes within our tech platform & CI/CD pipelines powered by AWS
- Innovate in Real-Time: Act as a core member of an agile, fast-moving team, rapidly pivoting between automated performance testing, security validation, and complex commerce workflow verification
Skills
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field (Targeting rising Juniors, Seniors, or Graduate students)
- Technical Interest: Exposure to (or a strong desire to master) SDLC, AI/Agentic Engineering, test automation
- Coding Foundation: Basic proficiency in Java, JavaScript/TypeScript, or Python
- Curiosity: A passion for understanding how complex systems break and an interest in how AI is changing the way we write software
- Courage: The drive to jump into a 'startup-feel' environment where you are expected to contribute code and ideas from day one
- Compassion: A commitment to collaborative problem solving and active listening within a global team
- The 'AI Edge': Familiarity with Generative AI tools (like Sourcegraph Coy, Amp, Cursor, Cline, Claude Code, GitHub Copilot) and an interest in how they can be used to generate smarter test cases
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