Robotics & Physical AI Architect

• Does this position require a security clearance? No
• Years 10+ years
• Applicants Less than 10 applicants
• Additional Info Visa / work permit sponsorship is not available for this position
• Applicants are required to read, write, and speak the following languages English

Role Overview

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.

Robotics Platform Scope

The architecture is expected to span cloud, edge, robot, AI, data, security, and enterprise integration layers. Representative platform capabilities include:

Representative Robotics Platforms / Capabilities

Fleet management; mission planning and orchestration; digital twins; remote operations; telemetry and observability; highly available distributed services.

Robot, Edge & Middleware

Robot identity and provisioning; edge and on-device runtimes; ROS 2, DDS, MQTT, OPC-UA, VDA
5050, OpenRMF, and similar middleware and protocols; secure robot-to-cloud communications.

Physical AI, Data & Simulation

Multimodal AI;
Vision-Language-Action (VLA) models; embodied AI; reinforcement learning; world models; AI infrastructure and MLOps; robotics data platforms;
Isaac Sim, Gazebo, replay, analytics, and benchmarking.

Security & Enterprise Integration

Zero Trust; authentication and authorization; secure communications; OTA updates; device lifecycle management; APIs, SDKs, protocols, and integration patterns for enterprise and industrial systems.

This list is representative, not exhaustive; the role is expected to create reusable architecture across heterogeneous robot platforms rather than own every robot implementation.

• 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.
• 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, VDA
  5050, 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…