Software Engineer, Verification and Validation

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 Develop tooling and instrumentation that make complex robot behavior easier to understand Convert field failures and difficult-to-reproduce issues into deterministic regression tests whenever possible Partner with subsystem owners to verify fixes and prevent recurrence 5. Drive System Reliability and Release Readiness Partner closely with autonomy, robotics software, hardware, systems, and field teams throughout the development lifecycle Identify integration and reliability risks early and ensure they are represented in the validation process Build dashboards, scorecards, and automated evaluations that provide a clear view of system health and capability maturity Help define release gates based on measurable system performance rather than subjective readiness Continuously improve the V&V process as the autonomy stack, robot platforms, and deployment environments evolve