Software Engineer, Verification & Validation

About the role

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.

What You'll Get to Do

1. Own End-to-End Robot Verification & Validation

- Design and execute verification and validation strategies for complete robotic systems, from individual capabilities through full autonomous missions

- Develop clear acceptance criteria, performance metrics, and test methodologies for new robot capabilities

- Validate system behavior across multiple robotic platforms, environments, and operating conditions

- Build repeatable qualification and regression processes that allow new capabilities to ship without compromising existing functionality

- Establish a clear understanding of what “deployment-ready” means and provide quantitative evidence that systems meet that bar.

2. Build Scalable Robotics Test Infrastructure

- Develop automated test infrastructure spanning simulation, hardware-in-the-loop, lab testing, and full robot operation

- Create reusable test scenarios and evaluation frameworks that exercise autonomy under nominal, edge-case, and failure conditions

- Build tools for experiment execution, telemetry collection, automated analysis, visualization, and reporting

- Improve the reproducibility of robot testing so failures can be recreated, diagnosed, and verified efficiently

- Help move validation from individual one-off tests toward continuously running, scalable system evaluation

3. Validate Real-World Robot Behavior

- Design tests that expose robots to the uncertainty and variability encountered in real deployments

- Exercise systems across changing terrain, obstacles, environmental conditions, sensor degradation, communication failures, compute limitations, and other realistic disturbances

- Evaluate not only whether a robot succeeds, but how reliably, safely, and consistently it behaves across repeated trials

- Identify performance boundaries and characterize where system behavior begins to degrade

- Work directly with physical robots in the lab and field to reproduce difficult system-level failures

4. Turn Failures Into Engineering Signal

- Debug failures across autonomy, sensing, state estimation, planning, control, system integration, compute, networking, and hardware boundaries

- Use telemetry and experimental data to isolate root causes rather than simply identify symptoms