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. 
 
 
 
 
 Join a team dedicated to redefining the way humans and robots collaborate. As a DevOps Infrastructure Engineer, you will have a unique opportunity to shape the infrastructure that powers the next generation of robotics systems. This is a chance to combine technical expertise with innovation, creating robust, scalable, and secure platforms that support real-world robotics applications. 
 
 
 
 
 In this role, you’ll play a critical part in designing and managing infrastructure for robotics control and monitoring, ensuring seamless operations and a great user experience.
What You Will Get To Do
• Architect, deploy, and manage cloud and on-premises (on-robot) infrastructure to support robotics operations and monitoring systems.
• Develop and maintain scalable CI/CD pipelines to ensure efficient development and deployment cycles.
• Implement and manage observability solutions to monitor system performance, diagnose issues, and ensure reliability.
• Optimize infrastructure for real-time robotics applications, focusing on low latency, high availability, and robust fault tolerance.
• Implement and enforce best practices for infrastructure security, including secure communication between robots and backend systems.
• Automate routine tasks, improve processes, and enable rapid scaling of infrastructure to meet growing demands.
• Collaborate closely with software engineers, robotics experts, and other stakeholders to build environments that foster innovation and efficient workflows.
What Will Set You Apart
• Experience working with robotics frameworks (e.g., ROS), IoT platforms, or real-time data pipelines.
• Experience working with AI-powered robotic systems or embedded AI platforms.