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Mantari

Weather intelligence platform with AI forecasting

Mechanical Engineer Intern

Design EngineeringConfirmed live in the last 24 hours
Internship · Summer 2026Los Angeles, CA, USANo salary listed

About the Job

Requirements

  • The candidate must have junior or senior academic standing.
  • The candidate must be currently enrolled in a bachelor's degree program in Engineering, Physics, or a related technical field.
  • The candidate must have a strong understanding of core engineering concepts and completed coursework in statics, dynamics, mechanics of materials, and deformable bodies.
  • The candidate must have experience using engineering tools such as computer-aided design (CAD), MATLAB, and/or LabVIEW.
  • The candidate must have strong written and verbal communication skills and be able to work in a team-oriented environment.

Responsibilities

  • Design and develop sensor hardware and mounting for field-deployable platforms from concept through completion.
  • Support test campaign planning and execution, including test fixture design and data analysis.
  • Create computer-aided design (CAD) models and detailed drawings, and perform basic engineering analysis such as finite element analysis (FEA) and thermal analysis.
  • Lead design reviews and collaborate with multidisciplinary teams to meet hardware and integration requirements.
  • Perform hands-on fabrication tasks or support prototyping and assembly operations as needed.
  • Support field test operations, including hardware setup, deployment, and on-site troubleshooting.

Desired Qualifications

  • Previous internship or co-op experience in a technical role.
  • Experience with fabrication or machining, manually or using computer numerical control (CNC).
  • A record of achievement in technical extracurricular activities such as Rocket Club or Formula SAE.

About the Company

Mantari

Weather intelligence platform with AI forecasting

Mantari builds an AI-native, full-stack weather intelligence platform delivering Weather-as-a-Service with data streams, models, and decision tools. It uses proprietary sensors for sea, air, and land plus a cloud data layer, with autonomous edge sensing fleets and deep learning models to produce near real-time forecasts and APIs for operations. It differentiates itself by end-to-end integration from sensors to decision APIs and embedded engines, serving government agencies and high-value commercial sectors like maritime, energy, and logistics. Its goal is to replace legacy public-sector weather systems by turning environmental signals into actionable intelligence for decision-making.

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