Physical AI Solutions Architect

Job Description:

Key Responsibilities

• End-to-End Physical AI System Architecture. Define full-stack architectures spanning sensors, perception, localization, planning, control and actuation. Design Sense–Think–Act pipelines with strict latency and determinism constraints. Evaluate hardware/software partitioning, especially CPU vs GPU vs FPGA mapping
• Customer Engagement & Solution Delivery: Lead technical engagements with physical AI customers. Translate customer requirements into architecture proposals and solution roadmaps and deliver: Technical presentations and executive briefings System-level design recommendations Competitive positioning vs GPU/CPU alternatives
• Technical presentations and executive briefings
• System-level design recommendations
• Competitive positioning vs GPU/CPU alternatives
• Proof-of-Concept (PoC) & Prototyping: Drive PoC development to validate architecture and performance, and work with engineering teams to build demos
• Physical AI Strategy & Market Development: Analyze physical AI market trends, customer needs, and competitive landscape Define physical AI solution stack (hardware + software + middleware) Reference architectures for key segments
• Analyze physical AI market trends, customer needs, and competitive landscape
• Define physical AI solution stack (hardware + software + middleware)
• Reference architectures for key segments
• Contribute to white papers, technical blogs, and training materials

Qualifications:

• Must have BS/MS, PhD preferred in Robotics, Electrical Engineering, Computer Engineering, Computer Science, or related field.
• 10+ years' experience in physical AI including robotics, drones, AI systems, or solutions architecture
• Strong expertise in one or more domains: Robotics systems and AI/ML for robotics.
• Experience with: ROS / ROS2 FPGA platforms (RTL, HLS) – Preferred, but not mandatory Real-time control systems and deterministic processing E2E robotics control loop architecture World modeling and point cloud generation Motion planning Physical AI SDK
• ROS / ROS2
• FPGA platforms (RTL, HLS) – Preferred, but not mandatory
• Real-time control systems and deterministic processing
• E2E robotics control loop architecture
• World modeling and point cloud generation
• Motion planning
• Physical AI SDK
• Familiarity with: Sensor fusion, localization, motion planning, control loops VLA and VLM models
• Sensor fusion, localization, motion planning, control loops
• VLA and VLM models

Travel Requirements

• 30%