Mechanical Robotics Manipulation Engineer

Amazon; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Amazon Frontier AI & Robotics seeks a hands-on Mechanical Engineer to design grippers, end-effectors, and data collection tools for robotics research. You will prototype quickly, iterating from sketches to functional hardware in a fast-paced R&D setting, collaborating with AI researchers to optimize mechanical properties for learned manipulation policies.

The role emphasizes cross-disciplinary teamwork, rapid iteration, and impact on the next generation of robot learning systems in a

Qualifications

• Bachelor’s degree in mechanical engineering, mechatronics, or equivalent.
• 3–5+ years designing mechanical systems for robotics, grippers, end-effectors, or complex electromechanical products.
• Proficiency in 3D CAD (SolidWorks, OnShape, or equivalent).
• Hands-on experience building prototypes, integrating hardware, and troubleshooting mechanical issues.
• Experience designing compact mechanisms with integrated actuation and sensing.

Responsibilities

• Design grippers, end-of-arm tools, and multi-finger hands for manipulation research and data collection.
• Develop UMI-style grippers and teleoperation hardware for data collection.
• Design fixtures, jigs, and mounting systems for cameras, sensors, and test setups.
• Integrate tactile sensors, force/torque sensors, and cameras into gripper assemblies.
• Rapidly prototype and iterate on manipulation hardware from concept to functional units.

Skills

Mechanical design

Robotics hardware

3D CAD

Rapid prototyping

Sensing integration

Cross-disciplinary collaboration

Education

Bachelor's degree in mechanical engineering

Master's degree in mechanical engineering / robotics (preferred)

Tools

SolidWorks

OnShape

Job description

Amazon Frontier AI & Robotics seeks a hands-on Mechanical Engineer to design grippers, end-effectors, and data collection tools for robotics research. You will prototype quickly, iterating from sketches to functional hardware in a fast-paced R&D setting, collaborating with AI researchers to optimize mechanical properties for learned manipulation policies.

The role emphasizes cross-disciplinary teamwork, rapid iteration, and impact on the next generation of robot learning systems in a