VP of Engineering

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.

Field

AI is seeking a Vice President of Engineering to lead the engineering organization responsible for scaling our autonomous robotics platform from early deployments to large-scale, sustained field operations. This role works across software platform and application engineering to ensure our robots, tools, and infrastructure are designed, tested, released, and operated with the rigor required for real-world autonomy  will partner closely with product, robotics, autonomy, and field operations leadership to align technical execution with a deployment-driven roadmap.

This role blends technical depth, organizational leadership, and operational discipline, with a strong emphasis on reliability, repeatability, and system maturity.

• Drive engineering efforts around a clear deployment-driven roadmap that supports reliable autonomous operation at increasing scale.
• Guide the technical direction and architecture of Field
  
  AI’s software stack, including on-robot systems, cloud services, data pipelines, and operator applications.
• Ensure clear interfaces between autonomy, platform, and application layers to support parallel execution and scale.
• Promote engineering practices that support long-lived, maintainable systems in safety-critical and operational environments.
• Partner with product and autonomy leadership to translate deployment goals into concrete technical milestones and execution plans.

• Lead teams responsible for robotics QA, release processes, and validation pipelines that support predictable and safe software releases.
• Guide development of automated testing infrastructure used for autonomy validation, regression testing, and deployment readiness.
• Oversee autonomy analytics systems that collect, process, and analyze data from real deployments to measure performance, reliability, and system limits.
• Guide software infrastructure efforts including CI/CD, dependency management, developer environments, documentation, and internal tooling.
• Support cloud infrastructure for fleet operations, monitoring, data ingestion, ML workflows, and observability.
• Collaborate with cybersecurity teams to ensure secure operation of on-robot and cloud systems.

• Lead application engineering teams building tools for robot operations, fleet management, mission planning, monitoring, and customer insight.
• Ensure tight integration between robots, cloud services, APIs, and user-facing applications.
• Support development of tools that reduce deployment effort, streamline onboarding, and automate triage and monitoring. Encourage clear separation between rapid operational support and longer-term platform evolution.

• Work with field and operations teams to ensure engineering systems support efficient, repeatable deployments.
• Improve feedback loops from live operations into engineering through metrics, tooling, and structured processes.
• Promote practices that reduce per-deployment engineering involvement over time through automation, documentation, and tooling.
• Support uptime, reliability, and autonomy performance tracking across deployed robots.

• Establish clear planning and execution rhythms that align engineering teams around shared goals and timelines.
• Improve visibility into team capacity, work in progress, and technical dependencies.
• Help teams balance near-term operational needs with longer-term system improvements.
• Support prioritization frameworks that keep engineering effort focused on the highest-impact work.