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.

Learn more at https://fieldai.com.

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.)

 

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.