Astranis

Astranis

Reliability Test Intern (Fall 2026)

San Francisco · Intern · Internship

Work authorization requiredDetected 2 days ago
PythonMATLABData AnalysisAerospace EngineeringHardware DesignResearchCommunication

About the role

  • With five satellites on orbit and many more set to launch soon, the company is servicing a backlog of more than $1 billion of commercial contracts.
  • Astranis has raised over $750 million from some of the world's best investors, from Andreessen Horowitz to Blackrock and Fidelity, and employs a team of 450 engineers and entrepreneurs.
  • As an Intern, you will have an amazing opportunity to work on hard problems - we pride ourselves on giving everyone at Astranis a chance to do meaningful work on complex projects, no matter their seniority.

Requirements

  • Currently pursuing a degree in Mechanical, Electrical, Aerospace Engineering, or a related technical field
  • Hands-on experience with environmental or reliability testing
  • Familiarity with test equipment such as test chambers and DAQs
  • Experience with data analysis tools in Python, MATLAB, or similar
  • Previous internship, research, or project experience in aerospace or hardware-focused environments
  • U.S. Citizenship, Lawful Permanent Residency, or Refugee/Asylee Status Required
  • (To comply with U.S. Government space technology export regulations, applicant must be a U.S. citizen, lawful permanent resident of the United States, or other protected individual as defined by 8 U.S.C. 1324b(a)(3))

Nice to have

  • Set up, operate, and monitor environmental test equipment, including thermal chambers, thermal vacuum systems, vibration shaker tables, and combined thermal vibe chambers
  • Execute reliability tests such as thermal cycling, thermal vacuum, and vibration

Compensation

  • The base pay for this position is $29.00 per hour.

Visa & Work Authorization

  • Citizenship, Lawful Permanent Residency, or Refugee/Asylee Status Required

This listing is sourced directly from Astranis's careers page and normalized into a canonical job model.