Physical AI Architect

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.

Company Overview
A cutting-edge robotics company is seeking a Physical AI Architect to advance warehouse automation through AI-driven robotics. The organization specializes in designing and deploying AI systems integrated with production hardware, bridging research innovation with real-world operational performance. The team focuses on high-throughput logistics, emphasizing reliability, scalability, and applied AI excellence.

About this Role
The Physical AI Architect will lead the design and deployment of AI systems for robotic perception, planning, and control. This senior technical role combines deep expertise in diffusion-based models, optimal control, and hardware integration, with the ability to translate theoretical methods into reliable, production-grade robotic systems. Success in this role is measured by deployed systems rather than papers.

Responsibilities

• Serve as the technical architect for the company’s Physical AI stack, owning end-to-end design of perception, planning, and control systems on production hardware
• Lead application of diffusion-based policy learning and optimal control for robot manipulation and picking, focusing on reliability and cycle-time performance
• Drive hardware integration across sensors, compute, and actuators, ensuring AI systems are co-designed with the physical platform
• Define the technical roadmap combining diffusion models and optimal control in the autonomy architecture, building internal alignment around this vision
• Partner with firmware, mechanical, and software engineering teams to ensure AI design decisions align with hardware constraints and operational realities
• Identify and resolve performance bottlenecks at the intersection of model inference, motion execution, and hardware throughput
• Mentor senior engineers and contribute to growing the technical depth of the autonomy team

Skills & Experience

• Demonstrated track record of shipping AI-powered systems to production, not just prototypes
• MS or PhD in Robotics, Computer Science, or a related field, or equivalent expertise demonstrated through deployed products
• Deep expertise in diffusion models applied to robot learning (e.g., diffusion policies, score-based generative models for behavior cloning or planning)
• Strong knowledge of optimal control theory and practice, including MPC, trajectory optimization, and feedback control design
• Practical understanding of combining diffusion-based learning with optimal control effectively
• Hands-on experience with hardware integration: sensor pipelines (RGB-D, force/torque, encoders), embedded compute (NVIDIA Jetson, ARM SoCs, FPGAs), and actuator interfaces
• Proficiency in Python and C++; familiarity with ROS 2 or equivalent robotics middleware
• Experience with real-time system constraints and deploying learned models on robot hardware
• Strong systems-level thinking, including maintainability, observability, and failure mode considerations
• Excellent communication skills for driving technical decisions across cross-functional teams
• Willing to work in-office in Charlestown, MA at least three days per week

Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals in globally.

1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
4. No Liability: SpringCube is not liable for inaccuracies.