Physical AI Architect

About Us

At Pickle Robot, we're on a mission to automate global supply chains with Physical AI. Our robots work alongside warehouse teams to unload trucks and containers - one of the toughest, most understaffed jobs in logistics - making the work safer, faster, and more efficient for the people doing it. Loading trucks comes next, followed by the Dill Autonomy Engine: generalized autonomy that will eventually orchestrate robots across entire logistics processes.

We're looking for a dynamic and driven Physical AI Architect to revolutionize the future of warehouse automation. This is a senior technical role for someone who is equal parts deep practitioner and pragmatic builder, with a track record of shipping advanced AI systems into production hardware.

If you measure success in deployed systems rather than papers, this role is for you.

What You'll Do

• Serve as the technical architect for Pickle Robot's Physical AI stack, owning the end-to-end design of perception, planning, and control systems deployed on production hardware
• Lead the application of diffusion-based policy learning and optimal control techniques to robot manipulation and picking tasks, with a focus on real-world reliability and cycle time performance
• Define the technical roadmap for how diffusion models and optimal control complement each other in Pickle Robot's autonomy architecture, and build internal alignment around that vision
• Drive hardware integration across sensors, compute, and actuators, partnering closely with firmware, mechanical, and software engineering teams to ensure AI systems are co-designed with the physical platform and grounded in operational realities
• Identify and resolve performance bottlenecks at the intersection of model inference, motion execution, and hardware throughput
• Mentor senior engineers and help grow the technical depth of the broader autonomy team

What You'll Bring

• Demonstrated track record of shipping AI-powered systems to production - we want to hear about systems you've deployed, not just prototyped
• MS or PhD in Robotics, Computer Science, or a related field, or equivalent demonstrated expertise
• Deep subject matter expertise in diffusion models applied to robot learning (e.g., diffusion policies, score-based generative models for behavior cloning or planning), plus strong command of optimal control theory and practice (MPC, trajectory optimization, feedback control) - and the architectural judgment to combine both 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 systems constraints and the performance tradeoffs of deploying learned models on robot hardware
• Strong systems-level thinking - you design for maintainability, observability, and failure modes, not just peak performance
• Excellent communication skills and the ability to drive technical decisions across cross-functional teams