Gather AI - Hardware / Software QA Engineer
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Requirements
• 3–5 years of QA or test engineering experience with genuine exposure to both hardware and software testing — not purely one side • Hands-on experience testing sensors (cameras, LiDAR, IMUs, or similar), including calibration, data quality assessment, and performance characterization • Embedded firmware validation experience: flash/update procedures, functional verification, regression testing, and failure mode analysis • Python proficiency for test automation, data processing, log analysis, and building test utilities • BS in Computer Science, Electrical Engineering, Computer Engineering, Mechatronics, or a related technical field; must be willing to work on-site in Pittsburgh • Experience with embedded platforms such as NVIDIA Jetson or ARM-based devices • Familiarity with communication protocols (UART, SPI, I2C, USB) and basic lab instruments (oscilloscopes, logic analyzers) • CI/CD experience for embedded or hardware-in-the-loop testing • Exposure to computer vision or ML pipelines — understanding how sensor data feeds into inference systems
Responsibilities
• Execute hardware-software integration tests in the Pittsburgh lab, validating sensor behavior, firmware functionality, and end-to-end data flow from physical devices through the software stack • Design and document repeatable test procedures for sensor integration and firmware validation across both the Drone and MHE Vision platforms • Identify and classify defects at the hardware-software boundary — distinguishing hardware faults, firmware bugs, driver issues, and application software errors — and route clearly to engineering owners • Build and maintain hardware-software integration test suites, including firmware validation, sensor calibration tests, and embedded release regression testing • Contribute to cross-platform test infrastructure that serves both product lines and establish defect classification and routing processes • Collaborate with systems engineers on integrated system tests and with the ML team on validating how sensor data feeds into perception pipelines
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