Senior QA Automation Engineer
Tulip Interfaces
| Company | Tulip Interfaces |
| Category | Engineering |
| Location | Budapest |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 21 May 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
This role is located in Budapest, Hungary - We are a hybrid work environment and are in the office 3+ days/per week.
Tulip brings modern technology and AI into manufacturing. Manufacturing accounts for almost 20% of the global economy and one in every seven workers on Earth, yet remains among the least digitized. More than a decade ago, we created the category to close that gap: AI-native software that puts cutting-edge tools directly in the hands of the people on the manufacturing front lines. Because we are human-centered and tech-obsessed, we are building a future of work where human ingenuity is amplified with the latest technology.
In January 2026 we became one of Boston's few new AI unicorns, reaching a $1.3B valuation. We're growing double digits in revenue year over year. The world's best manufacturers run on Tulip - turning inventory 10x faster, releasing gene therapies in hours instead of weeks, and training master craftspeople to forge engagement rings 5x more rapidly. Headquartered in Somerville, MA with offices in Germany, Hungary, Singapore, Japan, and Israel, we've been consistently named a World Economic Forum Global Innovator, a Gartner leader, and one of Built In Boston's "Best Places to Work."
We run the way we build: AI-forward, humanistic, and high ownership. We're looking for builders who are energized by complex problems, rapid cycles, and the unique opportunity to see their impact on a global scale.
About You
You are a Senior QA Engineer who operates at the intersection of deep product expertise and scalable automation engineering. You bring a systems mindset to quality - not just finding bugs, but designing the frameworks and feedback loops that prevent them at scale. You’re energized by ownership: leading cross-functional quality initiatives, mentoring teammates, and making deliberate technical choices that raise the bar across the engineering organization. You embrace AI-powered tooling not as a buzzword, but as a practical lever for accelerating coverage and reducing toil.
What skills do I need?
4-7+ years of QA experience with a balanced blend of manual testing and automation.
Hands-on expertise with Cypress and TypeScript/JavaScript automation frameworks.
Solid experience with API testing tools (e.g., Postman) and ideally automating API tests.
Strong exploratory/manual testing skills for complex SaaS products.
Experience integrating automated tests into CI/CD pipelines.
Proven, hands-on experience using AI-powered tools (e.g., Mabl, Momentic, Rainforest QA, or LLM-based assistants such as Claude) to improve testing quality, efficiency, or coverage.
Demonstrated experience leading quality initiatives both formally (e.g., owning a framework migration, rollout, or cross-team testing strategy) and informally (e.g., driving alignment between QA and engineering without direct authority).
Nice to have:
Experience designing or implementing performance/load testing strategies.
Exposure to other automation frameworks (Playwright, Selenium) or languages (Python).
Interest in IoT technologies or embedded systems.
Experience applying AI beyond test generation — e.g., intelligent failure triage, anomaly detection, or AI-assisted code review for test suites.
Key Responsibilities
Automation & framework ownership
Own and evolve Tulip’s Cypress + TypeScript E2E automation framework, driving expanded coverage across critical product surfaces.
Integrate automated test suites into CI/CD pipelines to deliver fast, reliable feedback loops for engineering teams.
Research and implement load and performance testing strategies to ensure system reliability at scale.
AI-powered quality
Apply AI-assisted tools to accelerate test generation, triage failures, and improve overall coverage velocity.
Identify opportunities to use AI beyond test creation — including intelligent failure analysis, anomaly detection, and AI-assisted code
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