Home solar-and-battery hardware with built-in energy management software
Lunar Energy designs residential solar-plus-storage systems paired with AI-driven software that orchestrates distributed energy resources. Its stack reveals a vertically integrated hardware company: Altium, SolidWorks, NX and Onshape for electrical and mechanical design, Rust, RTOS and Protocol Buffers for embedded firmware, and AWS with Grafana for fleet telemetry. Hiring is accelerating and overwhelmingly engineering-focused, with recurring pressure around field reliability and warranty costs.
Lunar Energy is a privately held company founded in 2020 and headquartered in Mountain View, California, with 201–500 employees. It builds an all-electric product ecosystem for homeowners, starting with an integrated solar and battery system that captures, stores and supplies power, including during grid outages. Each system ships with Gridshare, an AI-based platform that controls distributed assets like home batteries and EV chargers to maximize homeowner value and support grid optimization. The company hires in the United States and the United Kingdom.
Lunar Energy uses Altium Designer, SolidWorks, NX and Onshape for hardware design, Rust, RTOS, CMake and Protocol Buffers for firmware, plus AWS, Grafana, PostgreSQL, Jenkins and Salesforce.
Yes. Hiring is accelerating and concentrated in engineering, skewed toward senior and staff-level roles, across the United States and United Kingdom.
Current work includes firmware integration for the Lunar System, edge optimization, flash file system and communication stack firmware, manufacturing test fixtures, power electronics DFMEA and ETL data pipelines.
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Lunar Energy's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.