Overview
About Us
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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.
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
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
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