Tactile Sensing Hardware Engineer

Tactile Sensing Hardware Engineer

Mountain View, CA | Full-time | Onsite

At Proception, we're building the hardware layer for Physical AI.

Our products,
Pro Hand and Pro Glove
, enable robots to manipulate the world with human-like dexterity while collecting high-quality human demonstration data for next-generation robot foundation models.

We're backed by First Round Capital, Y Combinator
, and other leading investors, and are already shipping products to robotics companies and research labs worldwide.

We're looking for an exceptional hardware engineer to build the next generation of wearable tactile sensing systems
.

This is not another VR glove.

You'll help develop a glove capable of capturing high-resolution tactile information from the human hand—creating one of the world s most advanced interfaces for teaching robots.

You'll own problems spanning sensing, electronics, manufacturing, and system integration.

• Design flexible tactile sensing systems
• Develop pressure and force sensing technologies
• Build wearable electronic systems
• PCB design and embedded electronics
• Sensor characterization and calibration
• Manufacturing processes for wearable products
• Integrating tactile sensing with robotic data collection pipelines

You don t need experience in every area, but strong candidates typically have expertise in one or more of:

• Flexible or stretchable electronics
• Textile-integrated electronics
• Printed electronics
• Force / pressure / capacitive sensing
• Embedded systems
• Analog front-end design
• Signal processing
• Wearable devices
• Sensor manufacturing
• MEMS or sensor development

• Robotics
• Human-computer interaction
• Machine learning for sensing
• Consumer electronics
• High-volume manufacturing

You enjoy building things that don t exist yet.

You care deeply about engineering quality.

You like moving fast with a small team.

You want your work deployed on real robots—not left in a research paper.

You'll help build technology that could fundamentally change how robots learn from humans.

Instead of making robots imitate people through teleoperation, we're creating the sensing interface that lets robots learn directly from natural human manipulation.

If successful, your work could become part of the data collection pipeline behind the next generation of robotics foundation models.

Mountain View, California