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# Data Acquisition Module Lead

- **Company:** Harmattan AI
- **Location:** France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Signal Processing, Data Logging, Information Technology
- **Published:** November 9, 2026
- **Apply:** https://startup.jobs/data-acquistion-module-lead-harmattan-ai-10720178

## About the Role

* At least 6 years of experience in sensor data collection or field testing for perception or AI systems, ideally airborne.
 * Hands-on experience with EO/IR, radar or comparable sensors, including synchronization, calibration, storage and metadata.
 * Experience running multi-person field campaigns and leading a small team.
 * Track record of turning a model team's needs into a concrete data collection plan.
 * Piloting or flight operations exposure.
 * Safety-conscious, organized and pragmatic in the field, and comfortable arbitrating scarce flight time.
 * Clear communicator toward AI engineers who depend on the data you deliver.
 * A background in electronics, signal processing, robotics, physics or computer science is a plus; demonstrated field and data experience matters more.

## Description

As Data Acquisition Module Lead, you own how Harmattan collects the real-world sensor data that trains its onboard detection and tracking models. You run the module end to end: acquisition roadmap, sensor integration, field campaigns, and dataset quality and delivery to the computer vision and AI teams. You lead and grow a team of engineers while staying hands-on in the field. Every flight you run turns scarce test time into usable data., * Turn the data needs of the computer vision and AI teams into a prioritized roadmap of collection campaigns.

 * Plan and run field campaigns across sensors, scenarios, environments and data volumes.
 * Act as single owner and point of contact for every field session assigned to the module.
 * Own sensor integration and calibration on aircraft and ground systems, including time synchronization and logging.
 * Build the pipeline from raw field capture to curated, documented and versioned datasets, with quality checks before leaving the field.
 * Define dataset acceptance criteria and clear handovers with the computer vision team and the annotation provider.
 * Arbitrate flight slots and test field access with Flight Operations and Validation to maximize usable data per flight.
 * Structure the team's work, hire the profiles the module needs to grow, and mentor engineers.
 * Stay hands-on by operating alongside the team in the field and reporting regularly on progress, dataset coverage and gaps.