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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
In this role, you will develop sensors that capture physical details of the world with accuracy inside real hardware, enabling Mecka to collect richer data across its global capture network. The work spans sensor development (sensing physics, materials, fabrication, and characterization) and sensor integration (readout electronics, calibration, signal processing, packaging, and system validation).
Key responsibilities include:
- Developing new sensors or integrating commercial ones using controlled experiments to select and validate sensing approaches
- Building test fixtures and characterizing sensitivity, noise, hysteresis, drift, and sample variation
- Developing calibration and signal-processing methods to turn raw signals into reliable measurements
- Integrating sensors with electronics and synchronized recording systems
- Validating performance after packaging and repeated use to ensure durability outside the lab
- Documenting methods for reproducibility by the hardware team
Requirements:
Must-have:
- 5+ years of experience developing or integrating physical sensors
- Hands-on experience with tactile, force, pressure, or other physical sensors, with understanding of underlying sensing physics
- Practical skills in laboratory instrumentation and data acquisition, with depth in either sensor fabrication/materials testing or readout electronics/system integration
- Ability to design controlled experiments and distinguish useful signals from noise, fixture effects, and measurement error
- Proficiency in Python or similar language for signal processing, analysis, and visualization
- Knowledge of calibration, measurement uncertainty, and how packaging affects sensor performance and durability
Nice-to-have:
- Experience taking sensing technology from laboratory samples into repeatable manufacturing
