Make Rain
Field Operations Specialist
Salt Lake City, UT · Full-time
Sponsorship not specified$65k-$65kDetected 21 days ago
LogisticsSensorsGIS
About the role
- Rainmaker's meteorologists have identified an overnight seeding window within the mountain range of your area of operations.
- You've got about ten hours to turn supercooled liquid water into the snow that will eventually melt and feed into the aquifers, farms, and communities that need water now more than ever.
- At headquarters, your team scouts the mission and loads the F-150 with drones, generators, laptops, and a Starlink.
Responsibilities
- At 10,000 feet, the drone releases seeding particles into supercooled clouds, coaxing ice into existence, nudging the atmosphere toward something it was almost ready to do on its own.
- As dawn breaks, you pack up and drive home before sunrise to sleep, ready to do it again when given the chance.
- We develop and integrate radar validation, weather-resistant UAS, numerical weather
- Rainmaker is building something that hasn't existed before.
Requirements
- Experience in field operations, aviation, military, emergency management, construction, or similar hands-on environments
- Experience operating or supporting UAS
- Experience handling logistics, inventory, or supply
- Familiarity with GIS tools and spatial data (e.g., ArcGIS, QGIS, Google Earth)
- Required
- Willingness and ability to work in remote, physically demanding, and weather-exposed environments
- Strong attention to detail and discipline in following procedures
- Reliable, self-directed, and able to work with minimal supervision
- Ability to work effectively as part of a small field team
- Comfort with frequent travel, including to remote or international locations
- Valid driver's license
- Preferred
- Mechanical or technical aptitude, including equipment maintenance and troubleshooting
- FAA Part 107 Remote Pilot Certificate
Compensation
- Hourly, $65,000k annual equivalent + overtime
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This listing is sourced directly from Make Rain's careers page and normalized into a canonical job model.