Member of Technical Staff – Mechanical Engineer, Manipulation, Frontier AI & Robotics

We are seeking a Member of Technical Staff - Mechanical Engineer to design and develop robotic manipulation hardware within a frontier AI and robotics research lab. You will own the mechanical design of manipulation-focused hardware end-to-end: grippers, end-of-arm tooling (EOAT), multi-finger hands, UMI-style grippers, and data collection fixtures - all built to enable AI researchers to collect high-quality demonstration data and validate learned manipulation policies. This role sits at the intersection of mechanical design and AI research. You will be the person who builds the physical tools that make robot learning possible, rapidly prototyping and iterating on end-effectors and data collection hardware, working hand-in-hand with AI/ML researchers to understand what mechanical properties (compliance, sensing, geometry) actually matter for learned manipulation policies. The ideal candidate is hands-on, moves quickly, is motivated by learning across domains, and is as comfortable sketching a new gripper concept as they are debugging a real-world manipulation experiment. You thrive in fast-paced R&D environments where requirements evolve quickly, and you are genuinely curious about how your design decisions influence data quality and policy performance. If you want to directly shape the hardware that enables the next generation of robot learning, this role is for you. What You Bring: - A hands-on, build-first mindset: comfortable prototyping, testing, and iterating rapidly in a research environment. - Comfort designing in environments where requirements evolve quickly and hardware needs to be rethought from week to week - Genuine interest in how mechanical design choices such as compliance, geometry, sensing integration, etc. impact data quality for robot learning - Familiarity or curiosity about imitation learning, teleoperation, or data collection for manipulation - A collaborative and communicative working style, especially in multi-disciplinary research environments spanning AI, controls, and perception - A passion for robotics and advancing the state of the art in dexterous, capable manipulation systems Key job responsibilities - Design grippers, end-of-arm tools, and low DoF multi-finger hands for robotic manipulation research and data collection. - Develop UMI-style grippers and teleoperation hardware for collecting manipulation demonstration data. - Design fixtures, jigs, and mounting systems for cameras, sensors, and manipulation test setups. - Integrate tactile sensors, force/torque sensors, and cameras into compact gripper assemblies. - Rapidly prototype and iterate on manipulation hardware — from concept sketches to functional grippers in days/weeks. - Partner with AI researchers to understand what mechanical properties (compliance, sensing, geometry) matter for learned manipulation policies. - Design data collection devices, such as UMI grippers, that enable repeatable, high-quality demonstration capture. - Conduct mechanical testing of gripper performance (grasp force, compliance, durability, repeatability). - Apply DFM/DFA to scale successful gripper designs from one-offs to small batches (10s–100s of units). - Own cable routing, actuation, and sensing integration for compact end-effector designs. - Support hands-on builds, debug sessions, and real-world manipulation experiments. - Collaborate with controls, perception, and AI teams to ensure hardware meets research needs.

Frontier AI & Robotics (FAR) is the team at Amazon building the next generation of embodied intelligence. FAR drives the development and implementation of advanced AI models within Amazon’s operations that enable robots to see, reason, and act on the world around them, supporting a number of different warehouse automation tasks.