Electrical Engineer

The next ten years of AI will not be won in software. They will be won in the physical world: in factories, hospitals, kitchens, fields, and homes. The companies that own that data will own the century.

MicroAGI is building it. We are the data layer for physical AI.

Our models learn from data captured in the physical world, by hardware we design and build ourselves. As Electrical Engineer, you will own that hardware end to end, from sensor choice and PCB to firmware, so that the data reaching our ML team is clean, synchronized, and trustworthy.

• Design, build, and integrate the hardware that captures the data our models learn from: sensors, motion-capture rigs, custom IoT setups, and prototype robots.

• Own the firmware and embedded-software layer: real-time data collection, multi-sensor sync, calibration, and time-stamping.

• Spec and select sensors and compute for new hardware projects, balancing physical constraints against downstream ML needs.

• Work shoulder to shoulder with ML engineers. Your hardware delivers their training data, and bad sensor choices haunt them for months.

• Prototype fast: bench builds, custom rigs, breadboard to working device in days, not quarters.

• Up to 5 years building production-grade hardware or IoT systems.

• Strong embedded-software skills: C/C++, RTOS, and microcontrollers (STM
  32, ESP
  32, NXP, or similar).

• Experience integrating sensors: IMUs, cameras, depth sensors, force sensors, or similar.

• A strong understanding of how hardware decisions affect data quality, especially for EMG/EEG sensing: electrode placement, sample rate, filtering, noise floor, and motion artifacts. You know that a great model on a noisy signal still loses to a decent model on a clean one, and you design accordingly.

• Comfortable with PCB design and hardware debugging (logic analyzer, oscilloscope), with at least one foot in firmware.

• Genuinely curious about ML: you read papers, you have trained at least one model end to end, and you understand that dataset quality beats model architecture choice.

• High agency: you don't wait to be told what to do. You are comfortable in 0-to-1 ambiguity, where the spec is "figure out what the spec should be".

• Discreet: you handle confidential hardware roadmaps and customer data with care.

• Fluent in English. German is a plus.

• A background in robotics, motion capture, or wearable devices.

• Experience with real-time or hard-deadline systems: audio, video sync, or robotic control loops.

• Familiarity with ML training pipelines. Even if you don't run them, you can read a training script.

• Open-source firmware or hardware contributions.

• Experience scaling from prototype to small-batch production: DFM, vendor management, and certification.