Verification and Validation Software Engineer

Who are We?

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.

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We are looking for a Verification and Validation Software Engineer to own the quality bar for Field AI’s off‑road autonomy, legged, and humanoid robotics stacks—designing strategy, building tooling, and shipping the evidence that proves we’re safe and ready for the real world. You’ll implement scalable scenario‑based testing (SIL/HIL + high‑fidelity simulation), stand up CI pipelines and metrics that catch regressions early, and lead structured field validations where the terrain is messy and the stakes are real.

Day to day, you’ll partner with perception, planning, controls, and hardware to define clear entry/exit criteria, instrument systems for truth, and turn logs into actionable insights that raise reliability every week. If you love breaking things so customers don’t have to, care deeply about measurable safety, and want your rigor to translate into robots that confidently tackle the world, this is the role for you.

• Own end-to-end V&V for autonomy stacks
• Define test strategies for perception, planning, and control across vehicle and robot platforms
• Build SIL/HIL pipelines for continuous regression and performance tracking
• Establish entry/exit criteria, traceability, and sign‑off gates
• Level up our simulation & scenario coverage
• Develop realistic, adversarial scenarios (terrain, weather, lighting, sensor faults)
• Create large‑scale batch tests using scenario generators and digital twins
• Calibrate sim-to-real via data replay and hardware characterization
• Engineer reliable test infrastructure
• Implement CI/CD integrations (build, artifacting, test orchestration, dashboards)
• Develop tooling for log capture, triage, and automated failure bucketing
• Track metrics: requirement coverage, code coverage (incl. MC/DC), and safety KPIs
• Prove safety and reliability in the field
• Plan and run structured field validations for off‑road, legged, and humanoid use cases
• Execute fault‑injection and degraded‑mode testing with clear escalation paths
• Produce evidence packages for internal design reviews and external stakeholders

• 2–5+ years in software or robotics V&V, QA, or test infrastructure (or equivalent research/industry experience)
• Strong coding in Python and/or C and/or C++ for test harnesses, automation, and analysis
• Hands‑on with ROS/ROS 2, bag tools, and robotics logging/telemetry workflows
• Experience designing SIL/HIL setups, simulators, and scenario‑based testing at scale
• Familiarity with autonomy components (perception, localization, mapping, planning, control)
• Proficiency with CI/CD (e.g., Git Hub Actions, Git Lab), containers, and build systems (CMake)
• Comfort with sensors and data: cameras, LiDAR, IMU, joint/force sensors; calibration & synchronisation basics
• Clear, concise technical writing: test plans, results summaries, risk registers, and release notes
• A pragmatic, evidence‑driven mindset and bias for automation over manual toil

• Domain experience in off‑road autonomy (unstructured terrain, slip, wheel/track dynamics)
• Experience with legged/humanoid robots (whole‑body control, contact/force validation)
• Deep simulation chops:
  Isaac Sim, Gazebo custom scenario engines
• Fault‑injection and robustness testing (sensor dropouts, time skew, GPS denial, actuator faults)
  Statistical testing & evaluation: scenario coverage metrics, outlier analysis, reliability modelling
• ML evaluation experience (perception KPIs, dataset curation, drift detection, labeling QA)
• Tooling you can show: log diff/visualisation tools, test dashboards, scenario libraries
• Field test operations in challenging environments; strong safety discipline and test readiness reviews