Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.
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What You Have
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent professional experience).
- 5-8 years of experience.
- Strong software engineering fundamentals, with professional experience writing clean, maintainable code in TypeScript, JavaScript, or another modern object-oriented/functional language.
- Deep hands-on experience with Playwright, including advanced patterns like Page Object Models, network interception/mocking, custom fixtures, and multi-auth state handling.
- A "shift-left" engineering mindset, with a proven track record of embedding with developers, reviewing PRs for testability, and advocating for automated coverage over manual QA.
- Solid understanding of modern web architectures and APIs, including the ability to debug network calls, inspect DOM elements, and write automated tests directly against REST or GraphQL APIs.
- Experience managing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, CircleCI) and integrating automated test execution seamlessly into developer delivery workflows.
The Extras That Set You Apart
- Experience building Playwright infrastructure for large or complex products, such as microfrontend architectures, real-time data streaming web apps, or dashboard systems handling high-frequency state updates.
- Background working in robotics, IoT, or hardware-adjacent software spaces, where automated UI/API tests interact with physical devices, hardware emulators, or simulation environments.
- Advanced Docker and cloud infrastructure experience (AWS/GCP), specifically around scaling test agents or orchestrating localized environments for isolated end-to-end testing.
- Aligning Test Quality with Production Quality by identifying testing gaps and improve product observability.