Embedded Test Automation Engineer

About the role

ABOUT US Aeris Technologies, Inc., a subsidiary of Project Canary, develops and commercializes advanced, laser-based gas analyzers for trace gas monitoring across environmental, industrial, laboratory, and field applications. Aeris instruments deliver high-precision, real-time measurements of greenhouse gases, atmospheric pollutants, and natural gas leak indicators, helping customers collect reliable data in fixed, mobile, handheld, and aerial configurations.
As part of Project Canary, Aeris supports a broader climate technology platform that helps energy companies improve and report on their emissions footprint. Project Canary combines high-fidelity sensors, data from multiple technologies and sources, and proprietary analytics to deliver actionable insights that help operators stop leaks faster, reduce risk, streamline reporting, and differentiate their operations for key stakeholders.
Aeris’s technology brings lab-grade gas sensing performance into the field, supporting applications in environmental monitoring, industrial safety, air quality, research, and emissions measurement.
About the Role
The Embedded Test Automation Engineer will build and maintain the automated production test environment for our embedded Linux instruments. This role connects hardware, embedded software, lab systems, and cloud infrastructure to support reliable device provisioning, calibration, testing, and recovery.
You will develop tools and workflows that identify and network-boot devices, configure storage and file systems, install software, and validate system functionality. You will also integrate device calibration results and configuration parameters with cloud-based systems to maintain accurate production records and support disaster recovery.
We’re looking for an engineer who enjoys solving problems across hardware and software and can turn complex production processes into reliable, repeatable automation.

Build and maintain an integrated automated test environment for embedded Linux hardware.
Automate device identification, network booting, disk partitioning, file system configuration, and software installation.
Develop secure workflows that store and retrieve device-specific calibration results and instrument configuration data from cloud-based systems.
Design and expand automated test suites using Python, Pytest, LabVIEW, or similar tools.
Develop Python tools that integrate with existing LabVIEW and Python calibration systems.
Troubleshoot issues across hardware, operating systems, software, and network connections.
Partner with hardware, firmware, test, manufacturing, and cloud teams to improve device bring-up, testing, and configuration.
Improve the reliability, repeatability, and maintainability of production test processes.

Required Qualifications:
4–7 years of experience in embedded systems, test automation, manufacturing test engineering, or a related field.
Strong Python programming skills and experience building maintainable automation tools.
Hands-on experience using Pytest or a similar framework for hardware, system, or integration testing.
Working knowledge of embedded Linux, including boot processes, network booting, storage partitioning, and file system configuration.
Experience integrating systems through APIs, cloud databases, or other data services.
Ability to diagnose complex issues across hardware, operating systems, software, and networks.
Experience working across engineering disciplines to deliver production-ready systems.
Practical approach to documentation, error handling, and long-term maintainability.
Nice to Have:
Experience developing or maintaining LabVIEW automation and integrating LabVIEW with Python.
Familiarity with hardware-in-the-loop test environments and automated laboratory equipment.
Experience applying CI/CD practices to embedded systems or physical hardware testing.
Experience supporting production provisioning, calibration, or manufacturing test systems.
Familiarity with cloud-based data storage, device configuration management, or disaster-recovery workflows.