Amazon LEO is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Amazon LEO will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity.

We are seeking a Scientist to own analytics, scientistic research, validation, and development of our phased array systems across customer terminals and satellite-deployed terminals. In this role, you will transform raw test and telemetry data into actionable insights and automated checks that ensure every deployed system performs to expectations.

You will build data and ML pipelines that detect calibration issues, quantify array performance, validate new algorithms, and increase the effectiveness, reproducibility, and automation of our antenna, calibration and deployment workflows. This role sits at the intersection of array processing, data science, and systems engineering, working closely with calibration/validation, phased array systems, RF communications, and test engineering teams.

Key job responsibilities
- Own metrics and data models that describe end-to-end calibration and system performance for customer terminals and satellite-deployed terminals, from factory test through field deployment.
- Design and implement ML and statistical methods (e.g., anomaly detection, drift detection, predictive failure models, classification/regression) to identify mis-calibration, degraded performance, and emerging issues in large fleets of terminals and arrays.
- Develop visualization and analytics tools (dashboards, reports, interactive notebooks) to help engineers quickly understand array performance, calibration residuals, and system margins across time, geography, and configurations.
- Work with antenna, RF systems, and calibration engineers to define quantitative acceptance criteria for calibration and system validation, and encode those criteria into automated checks and workflows.
- Design and run experiments and simulations that compare predicted vs. measured performance, closing the loop between link/array models and deployed hardware.
- Build and maintain data pipelines that ingest lab data, chamber results, manufacturing test data, and in-field telemetry into secure, high-quality datasets suitable for analysis and ML training.
- Prototype, test, and deploy machine learning and analytics applications in the cloud, partnering with software and systems teams to ensure solutions are scalable, maintainable, and integrated into existing tools and monitoring systems.
- Provide clear, data-driven recommendations to improve calibration algorithms, test strategies, and system design; communicate findings to technical and non-technical stakeholders.
- Mentor engineers and analysts in data best practices, experiment design, and interpretation of calibration and performance metrics.

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.

A day in the life
You will work with engineers to process large amounts of data and work through full designs to fully understand the Amazon Leo satellites and customer terminals from lab test benches to fully integrated customer terminals. Your tools and systems will prove out the performance of one of the most advanced communication systems ever built.

You’ll collaborate with RF and systems engineers, see your models run using real hardware data, and make daily decisions that directly affect the readiness of Leo’s payload and customer terminal products. The data you analyze and scalable systems you build will enable critical data collection, system analysis, and calibration pipelines that ensure Leo hardware performs flawlessly on Earth and in orbit.

About the team
Our team owns the full performance lifecycle of Leo’s antenna systems from early concept testing and calibration to final product release. We operate at the intersection of RF hardware, software automation, and large-scale system integration.

You’ll work closely with antenna, DSP, and system development engineers, contributing to test frameworks, manufacturing support, and performance validation. We leverage AWS tools and scalable software architectures to accelerate development, automate validation, and deliver reliable test systems used across the entire Leo organization.

If you want to work on real hardware, influence product performance, and see your work scale to millions of users worldwide this is the place to do it

RF Systems Scientist, Amazon Leo