Amazon's Frontier AI & Robotics team is looking to solve the problem of building complex robotic systems from the ground up, requiring a Mechanical Engineer with a deep understanding of structural and electromechanical design.
Requirements
- Strong proficiency in 3D CAD (e.g., SolidWorks, OnShape, or equivalent).
- Significant hands-on experience building prototypes, integrating hardware, and troubleshooting mechanical/electromechanical issues.
- Deep understanding of actuator and motor selection for complex robotic applications, including sizing for torque, speed, and thermal performance.
- Experience with wire/cable harness design and routing in compact mechanical systems.
- Familiarity with integrating sensing and compute elements into mechanical designs.
- Solid knowledge of DFM/DFA principles and exposure to volume production (50+ units).
- Experience with structural analysis practices and tools (e.g. FEA/Ansys), thermal analysis, and material selection.
Responsibilities
- Lead mechanical design of robotic subsystems and full platforms, including structures, joints, enclosures, and mechanisms for a research environment.
- Specify and integrate actuators and motors for high-torque density applications in high-degree-of-freedom systems.
- Design and route cabling and wire harnesses, ensuring reliability, serviceability, and thermal/electrical integrity.
- Contribute to thermal management strategies for motors, sensors, and embedded compute hardware.
- Integrate sensors such as lidar, stereo cameras, IMUs, tactile sensors, and compute modules into compact, functional assemblies.
- Prototype and test mechanical systems; support hands-on builds, debug sessions, and field testing.
- Conduct root cause analysis on system-level failures or performance issues and implement design improvements.
Other
- Bachelor's degree in Mechanical Engineering, Mechatronics, or a related discipline.
- 3+ years of experience designing mechanical systems for robotics or complex electromechanical products.
- A systems-thinking mindset with a strong grasp of cross-domain engineering tradeoffs.
- A bias toward action: comfortable building, testing, and iterating rapidly.
- A collaborative and communicative working style — especially in multi-disciplinary research environments.
