Robotics & Physical AI Architect

Robotics & Physical AI Architect As a Robotics & Physical AI Architect, you will define the technical architecture for a large-scale robotics platform supporting heterogeneous robots, AI workloads, cloud services, edge environments, and enterprise integrations. You will establish reference architectures, canonical APIs, protocols, SDKs, and integration patterns that enable multiple engineering teams to build on a consistent platform foundation. You will work across robotics software, distributed cloud platforms, AI systems, edge computing, networking, security, and developer platforms to build architectures capable of operating thousands of robots across diverse environments. The role requires strong systems thinking, deep software architecture experience, and the ability to influence technical direction across multiple engineering organizations. This list is representative, not exhaustive; the role is expected to create reusable architecture across heterogeneous robot platforms rather than own every robot implementation. What You'll Do • Define long-term architecture and technical strategy for robotics and physical AI software platforms spanning cloud, edge, and on-device environments. • Design scalable platform architectures that support autonomous robot fleets, heterogeneous robot vendors and form factors, AI workloads, and enterprise integrations. • Architect systems for: – Fleet management, robot identity and provisioning, and device lifecycle management. – Digital twins, mission planning and orchestration, and remote operations. – Real-time telemetry, observability, and highly available, fault-tolerant distributed services. – Software and model lifecycle management, including secure OTA updates and AI operationalization. – Robotics data platforms supporting simulation, replay, analytics, benchmarking, and AI model improvement. – Developer APIs, SDKs, protocols, and reusable integration patterns for robotics applications. • Define the cloud-to-robot control-plane architecture and canonical APIs that provide a consistent platform across heterogeneous robot implementations. • Design secure communication architectures between robots, edge devices, cloud services, and enterprise systems, including authentication, authorization, distributed identity, Zero Trust, and secure device lifecycle patterns. • Define interoperability patterns for heterogeneous robotics platforms using ROS 2, DDS, MQTT, OPC-UA, VDA5050, OpenRMF, and related robotics middleware and industry standards. • Partner with AI researchers to operationalize multimodal AI, Vision-Language-Action (VLA), embodied AI, reinforcement learning, world models, and future foundation models for physical AI systems. • Define architecture for AI infrastructure, MLOps, and model lifecycle management across cloud, edge, and robot environments. • Architect scalable data platforms for robotics workloads, including simulation, replay, analytics, benchmarking, telemetry, and iterative AI model improvement. • Define clear architecture boundaries and interfaces across cloud fleet services, edge computing platforms, and on-device software so capabilities can be deployed consistently across diverse robotics environments. • Establish reusable integration patterns across fleet orchestration, digital twins, telemetry and observability, robotics data platforms, AI infrastructure, and enterprise systems. • Define fleet observability and remote-operations architecture that connects robot telemetry with cloud and edge services for operational visibility and lifecycle management. • Architect the platform to support multiple robot classes, including autonomous mobile robots, humanoids, manipulators, drones, and industrial automation systems, without coupling the control plane to a single vendor or form factor. • Partner across robotics software, cloud infrastructure, edge computing, networking, security, AI, and developer-platform teams to operationalize a coherent end-to-end architecture. • Define security architecture for robots, including identity, secure communications, OTA updates, Zero Trust, and device lifecycle management. • Establish architecture patterns for highly available, fault-tolerant distributed systems spanning robots, edge platforms, and cloud services. • Evaluate robotics technologies, middleware, frameworks, simulation platforms, and industry standards to guide platform architecture decisions. • Drive technical direction across multiple engineering teams and mentor senior engineers and architects. Basic Qualifications • BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field. • 15+ years of software engineering experience, with significant experience architecting distributed systems operating at large scale. • Deep expertise in cloud-native software architecture, APIs, event-driven systems, microservices, and distributed platform design. • Strong programming background in C++, Go, Java, Python, Rust, or similar languages, with the ability to reason about architecture and implementation tradeoffs. • Experience with Kubernetes, containers, Linux, networking, and modern cloud platforms. • Experience designing highly available, fault-tolerant systems across distributed cloud and edge environments. • Strong understanding of systems security, authentication, authorization, distributed identity, and secure communications. • Strong systems thinking and the ability to work across robotics software, distributed systems, AI platforms, networking, and developer tooling. • Experience defining APIs, SDKs, protocols, and integration patterns for heterogeneous distributed platforms. • Experience reasoning across cloud, edge, and on-device architecture boundaries for large-scale systems. • Excellent written and verbal communication skills, including the ability to communicate architecture decisions across senior engineering and product audiences. Preferred Qualifications Experience in several of the following areas: • Robotics software platforms and fleet-scale robotics architecture. • ROS 2, DDS, MQTT, OPC-UA, VDA5050, OpenRMF, or similar robotics middleware, communication protocols, and interoperability standards. • Autonomous mobile robots, humanoids, manipulators, drones, or industrial automation systems. • Edge computing platforms and cloud-to-edge software architecture. • Real-time telemetry, observability, fleet management, and remote operations systems. • Digital twins and mission planning or orchestration platforms. • Distributed databases and robotics data platforms supporting simulation, replay, analytics, and benchmarking. • AI infrastructure, MLOps, and production model lifecycle management. • Reinforcement learning, Vision-Language-Action (VLA) models, world models, embodied AI, or multimodal AI systems. • Simulation platforms such as Isaac Sim or Gazebo. • Robot identity and provisioning, secure communications, OTA update systems, Zero Trust architecture, or device lifecycle management. • Cloud-to-robot control planes, canonical robotics APIs, and multi-vendor robot support. • Mission orchestration, remote operations, and lifecycle management for fleets operating across diverse environments. • Enterprise system integration and industrial automation environments. • Building reusable APIs, SDKs, protocols, and platform capabilities for heterogeneous robotics systems. Ideal Candidate The ideal candidate combines deep software architecture experience with a practical understanding of robotics and AI. You are comfortable working across operating systems, cloud infrastructure, edge computing, robotics software, distributed systems, AI platforms, networking, security, data platforms, and developer tooling. You think in terms of cloud-to-robot platforms rather than isolated robot implementations. You can simplify a fragmented robotics landscape into coherent architecture patterns for fleet control, digital twins, mission orchestration, telemetry, data, security, middleware, AI operationalization, and enterprise integration, and influence engineering organizations through technical leadership rather than organizational authority. You have a track record of building platforms that other engineers and product teams build products on. Areas of Technical Ownership • Robotics software architecture • Distributed systems • Cloud platforms • Edge computing • AI infrastructure and MLOps • Robotics middleware and interoperability • Fleet orchestration and remote operations • Digital twins • Security architecture and device lifecycle • Robotics data platforms, simulation, and replay • Developer platforms • Platform APIs, SDKs, and protocols • Enterprise integration • System scalability and reliability What Success Looks Like • Defined Oracle's reference architecture for OCI Robotics Cloud across cloud, edge, robot, AI, data, security, and enterprise integration layers. • Established the cloud-to-robot control-plane architecture and canonical APIs for heterogeneous robotics platforms. • Led the architecture for digital twins, mission orchestration, telemetry and observability, remote operations, and robot lifecycle management.