Mechanical Engineer - Wearable Systems
About the role
Design and build the physical architecture of a load-bearing wearable system, including enclosures, mounting structures, modular docking mechanisms, and cable routing for a wearable robotics platform. Hands-on, execution-focused role with rapid prototyping, wear testing, and close collaboration with fabrication and vendors.
Responsibilities
- Design adjustable, load-bearing wearable structures that accommodate a range of body sizes, optimizing for comfort and fatigue reduction during extended use
- Develop protective enclosures and mounting systems for embedded electronics, balancing structural integrity with weight constraints
- Design modular docking and retention mechanisms for removable hardware modules, including blind-mate alignment and tool-less serviceability
- Iterate rapidly using 3D printing and small-batch fabrication; own hands-on testing and mechanical debugging
- Collaborate with local fabrication and soft goods vendors to improve manufacturability
- Define cable routing and strain management systems that survive repeated flexion and allow removal for cleaning
Qualifications
- 3-5 years of mechanical design or hardware prototyping experience
- Strong proficiency in Fusion 360 or SolidWorks
- Experience designing enclosures, structural mounts, or load-bearing assemblies
- Hands-on prototyping experience with real-world durability and motion constraints
Skills
About Human Archive
Multimodal data provider for robotics and world modeling
We’re archiving the physical world for embodied intelligence by collecting and labeling aligned multimodal data. To build dexterous and perceptive robots that generalize robustly, we need massive amounts of real-world data across multiple modalities and environments. We have thought deeply about the fine line between biomimicry and its application to humanoid systems. Based on this research, we design and deploy custom hardware across residential and manufacturing settings. We then post-process the resulting data through internal QA, anonymization, and annotation pipelines to deliver diverse, high-fidelity datasets at scale to frontier labs developing robotics foundation models and general-purpose robotics companies. We believe we are at a historic inflection point, with a unique opportunity to leave a dent on humanity and reshape physical labor markets forever. That's why our team dropped out of Stanford and Berkeley and moved to Asia to collect the world’s largest annotated multimodal dataset.
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