Software Engineer, Verification and Validation

Job Description

Job Description

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.

We’re building autonomous robots that operate reliably in complex, unpredictable real-world environments and we need a robotics software engineer to help prove they’re ready. You’ll work across the full autonomy stack to design tests, build validation infrastructure, investigate system-level failures, and define measurable performance and release criteria. You’ll move between simulation, hardware-in-the-loop, and real robots to turn failures into engineering insight. If you love breaking complex robotic systems, understanding why they fail, and making them more dependable, this is your role.

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What You'll Get to Do

• 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.

• 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

• 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

• 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

• 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

• Bachelor’s, Master’s, or PhD degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a related technical field, or equivalent hands-on industry experience
• Strong software engineering fundamentals with proficiency in  C, C++, and Python for developing, integrating, debugging, and testing robotic systems
• Hands-on experience developing, integrating, testing, or debugging  physical robotic or autonomous systems
• Broad understanding of robotic systems, including sensing, state estimation, planning, control, communication, compute, and actuation
• Experience developing and debugging robotics software using  ROS 2 and distributed message-passing architectures
• Strong experience working in  Linux environments , with familiarity with real-time systems and RT-patched Linux kernels
• Proficiency with  Git and modern collaborative software development practices, including code review, branching, integration, and regression workflows
• Experience diagnosing complex  system-level failures where root causes may span software, middleware, networking, compute, sensors, and hardware
• Experience designing quantitative experiments, defining performance metrics, and analyzing robot telemetry and test data to evaluate system behavior and reliability
• Strong instincts for  testability, observability, reproducibility, and systematic debugging , with the ability to turn ambiguous robot behavior into concrete hypotheses, experiments, and actionable engineering work

The Extras That Set You Apart

• Experience developing system-level V&V, qualification, or release processes for autonomous robots, vehicles, drones, or other complex physical systems
• Experience building automated regression testing or large-scale evaluation infrastructure for robotics.
• Familiarity with simulation, hardware-in-the-loop, fault injection, or scenario-based testing
• Experience defining reliability metrics, operational performance envelopes, or quantitative release criteria
• Experience with automated telemetry analysis, experiment management, or fleet-level performance evaluation
• Experience testing robotic systems in unstructured, dynamic, or safety-critical environments
• Experience bringing new robotic capabilities from initial integration through production deployment
• A track record of finding subtle system-level problems that were difficult to reproduce or crossed traditional subsystem boundaries
• The ability and willingness to get hands-on with robots, understand the complete system, and go wherever the problem leads

Compensation

The salary range for this role is $135,000-$190,000. The actual offer for this position will be based on factors such as relevant experience, competencies, certifications, and how well the candidate meets the qualifications outlined above. Part of our compensation package also includes full benefits, equity, and generous time off.

Field AI Onsite Work Philosophy

At Field AI, we believe the most effective way to collaborate and solve complex challenges is by working together in person. This is a fully onsite role, and candidates will be expected to work from our Mission Viejo, CA office. In-person engagement is essential to our success, and we offer flexible working hours to support focus and work-life balance.

We are dedicated to fostering a diverse and inclusive workplace and encourage applicants from all backgrounds to apply

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.