Find your next role

Description

Amazon Leo is Amazon’s low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity.

The Leo RF Technologies team is looking for a Software Development Engineer who is excited to work at the intersection of low-level software, Linux-based systems, RF engineering, wireless communications, and AI-driven engineering productivity.

In this role, you will develop software, tools, automation, and AI-enabled workflows that help hardware and software engineering teams design, debug, validate, and operate complex RF systems. You do not need to come in as an RF expert, but you should have a strong software engineering foundation, a high learning velocity, and the drive to understand low-level Linux systems, embedded environments, 3GPP concepts, gNodeB architecture, and RF engineering fundamentals.

You will work closely with RF, antenna, modem, PHY/MAC, embedded software, manufacturing test, and systems engineering teams to identify productivity bottlenecks and build scalable software solutions. Your work will help engineers move faster, debug more effectively, improve documentation and knowledge discovery, and adopt AI in practical, high-impact ways across hardware and software development.

This is an individual contributor software development role for someone who enjoys ambiguous technical problems, can learn across disciplines, and wants to build tools that directly improve how complex satellite communication systems are engineered.

Export Control Requirement:
Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Key job responsibilities
- Develop AI-assisted engineering solutions for documentation search, debug support, test data analysis, issue triage, and engineering knowledge discovery.
- Work with low-level Linux environments, including LEP and embedded Linux-based platforms, to support RF and communication system development.
- Build automation and integration pipelines that connect engineering data sources, test systems, logs, documentation, and analysis tools.
- Partner with RF engineers to understand antenna, phased array, modem, gNodeB, 3GPP, and satellite communication concepts well enough to build useful software abstractions and tools.
- Create scalable backend services, APIs, data pipelines, and developer productivity platforms that improve engineering execution across multiple teams.
- Develop tooling that helps engineers debug hardware/software interactions across RF systems, embedded software, and communication protocol layers.
- Improve access to technical knowledge by building systems that index, organize, retrieve, and summarize engineering documentation and data.
- Collaborate with cross-functional teams to identify recurring manual workflows and replace them with robust software solutions.
- Write high-quality, maintainable code and documentation that can be adopted by engineering teams across Leo.

A day in the life
You may start the day reviewing feedback from RF engineers who are trying to debug a hardware issue and need better access to logs, test data, and system documentation. You might then work with embedded software teams to understand how a Linux-based platform exposes RF telemetry, followed by building a service that indexes that data and makes it searchable through an AI-assisted interface.

Later, you may meet with systems engineers to learn how 3GPP and gNodeB concepts map into Leo’s satellite communication architecture, then translate that understanding into tooling that helps teams validate assumptions, identify regressions, or accelerate root-cause analysis.

This role requires curiosity, technical depth, and the ability to move between software systems, hardware context, and AI-enabled productivity use cases. You will not just write code; you will help define how software and AI can make RF and hardware engineering teams more effective.