Amazon is building the next generation software, hardware, and processes that will run the global network of fulfillment centers that move many millions of units of inventory, and ensure that customers get what they want when we promised. We do this by building automated solutions to handle things that technology does well, while relying on people to handle things that humans do well. We are constantly innovating in the ways we are automating and accelerating the delivery of products and packages which includes building custom hardware and applying robotics, autonomy, supply chain optimization, machine learning, manipulation, image processing, and real-time data processing using distributed systems. The RDPI (Robotics Deployment and Packaging Innovation) Global Engineering team is seeking a talented Experimentalist to contribute to delivering transformational innovation to the business. You will be part of a team with the mission to ensure that robotics technologies maturing through the development pipeline are vetted and tested to identify and resolve issues upstream before large-scale deployment. Through rigorous test-to-fail methodologies, discrete event modeling, and comprehensive site integration testing design and analysis, you will bridge the gap between product design and field implementation, ensuring our automated systems meet performance targets in real-world operational scenarios. Key job responsibilities Problem-Solving & Methodology Development - Create systematic approaches and best practices for tackling complex, undefined problems across product development initiatives Experimentation and Prototyping - Characterize and validate 3D depth cameras for automated quality inspection systems, including designing precision measurement experiments, developing RMSE benchmarks, and evaluating sensor performance on reflective industrial surfaces - Design and lead in-depth experiments to verify and validate design hypotheses, ensuring scalability, efficiency, and process optimization - Drive fast-paced development cycles through rapid prototyping and iterative testing - Bridge gaps in product design and field implementation by translating test results into actionable design and process improvements Data Analysis & Modeling - Conduct statistical data analysis to extract insights and inform decision-making - Develop and utilize modeling and analysis tools, including discrete event simulations, to predict critical business impacts such as throughput and identify system bottlenecks Infrastructure & Tooling - Design and build lab infrastructure including hardware mockups and test fixtures to support continuous ideation Strategic Leadership - Partner with cross-functional leadership teams across the organization to establish technical vision, define experimental roadmaps, and drive strategic initiatives