Geospatial & RF Data Engineer for LEO Satellites

Amazon; Austin (TX); On-site; USD 129,000 - 175,000; Full time

Benefits offered by this job

RSUs

Health insurance

401(k)

Job summary

Amazon Leo is seeking a Systems Development Engineer to design and implement remote data pipelines for space-based RF links, enabling geospatial analysis and AI-driven anomaly detection. You will work across teams on end-to-end system integration for ground stations and user terminals.

The role emphasizes Python, SQL, AWS CDK, and ML techniques applied to terabytes of IQ data, with opportunities to influence link budget models and testing in a lab environment.

Qualifications

• Bachelor's degree in Electrical Engineering or related field.
• Strong CI/CD principles and AWS Cloud Infrastructure (CDK).
• Proficient in Python, SQL, PostgreSQL, PostGIS, JavaScript.
• Experience with remote sensing, data pipelines, and visualization libraries.

Responsibilities

• Design, build, and support remote sensor data collection pipelines from space-based RF links.
• Implement storage, search, and analysis for terabytes of IQ data from ground sites.
• Apply geospatial analysis to identify RF patterns and issues in space.
• Use AI/ML to detect RF/PHY anomalies from satellites on orbit.
• Automate lab validation of RF links and integrate real-world data into models.
• Collaborate with engineers to validate end-to-end system architecture.

Skills

CI/CD

Python

SQL

PostGIS

JavaScript

AWS

ML basics

Remote sensing

Data pipelines

Education

Bachelor's degree in Electrical Engineering

Master's or PhD in wireless communications

Tools

MATLAB

C/C++

Git

Job description

Amazon Leo is seeking a Systems Development Engineer to design and implement remote data pipelines for space-based RF links, enabling geospatial analysis and AI-driven anomaly detection. You will work across teams on end-to-end system integration for ground stations and user terminals.

The role emphasizes Python, SQL, AWS CDK, and ML techniques applied to terabytes of IQ data, with opportunities to influence link budget models and testing in a lab environment.