Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction.

We are seeking an Applied Scientist to develop innovative and scalable solutions in motion planning and feedback controls for complex robotic systems. In this role, you will take responsibility for developing robot physics models, performing analysis, and devising and implementing algorithms for the control and estimation of robotic systems. You will collaborate with a world-class team of experts in perception, machine learning, motion planning, and feedback controls to innovate and develop solutions for complex real-world problems.

As part of your work, you will investigate applicable academic and industry research to develop, implement, and test solutions that support product features. You will also design and validate production designs. To succeed in this role, you should demonstrate a strong working knowledge of physical systems, a desire to learn from new challenges, and the problem-solving and communication skills to work within a highly interactive and experienced team. Candidates must show a hands-on passion for their work and the ability to communicate their ideas and concepts both verbally and visually.

Key job responsibilities
- Conduct research, design, implement, and assess feedback control and motion planning algorithms, ensuring integration across various disciplines.
- Develop experiments and build prototypes to implement control algorithms, planners, and optimization techniques.
- Collaborate closely with software engineering teams to ensure scalable, real-time implementation of algorithms.
- Partner with cross-functional engineering teams to deploy algorithms from initial prototyping to production-level implementation.
- Engage with stakeholders across hardware, scientific, and operational teams to iterate on system design and implementation.