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