Posted at: 6 March

Test Engineer

Company

CompanyGather AI

Gather AI is a Pittsburgh-based B2B company specializing in AI-powered intralogistics optimization using autonomous drones for real-time inventory monitoring in the logistics and warehousing industry.

Remote Hiring Policy:

Gather AI primarily hires within the United States and may offer limited remote work opportunities, focusing on collaboration from its Pittsburgh headquarters.

Job Type

Full-time

Allowed Applicant Locations

United States

Job Description

About Us

Are you ready to build the future of supply chain? At Gather AI, we’re not just creating software; we’re pioneering a new era of warehouse intelligence. We’ve developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining “on-time, in full” delivery.

If you’re looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We’re leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.

About the Team

You’ll work most closely with the systems engineers on the MHE Vision platform. You’ll collaborate with the MHEV team on integrated system testing and debugging, with the ML team on validating model outputs against ground truth data, and with Customer Support and Product Management on field-reported edge cases and quality priorities.

About the Role

We are looking for a Test Engineer to join our Pittsburgh team and strengthen the testing and validation layer for our MHE Vision platform. You will work hands-on with real data and real systems — analyzing large transaction datasets to extract ground truth, building and maintaining regression test infrastructure, and contributing to integrated system tests in our lab.

This is a high-impact early-career role with real ownership and visibility. Your analysis directly improves product quality for customers, and you’ll have the opportunity to grow into test automation, systems engineering, or QA leadership as the company scales.

What You’ll Do

  • Manually analyze large transaction datasets (100+ records) to extract ground truth, identify anomalies, mismatches, and edge cases, and validate system outputs against expected behavior
  • Build and maintain regression test infrastructure, integrating new edge cases sourced from field deployments into the existing test suite
  • Participate in integrated system tests in the Pittsburgh lab alongside systems engineers, executing test procedures and documenting results
  • Perform first-level root cause analysis (RCA) on test failures and route issues clearly to the appropriate engineering owners
  • Develop structured processes for collecting and cataloging field edge cases into repeatable test cases
  • Contribute to continuous improvement of the test and validation pipeline through clear documentation, reporting, and collaboration with engineering leads

What You’ll Need

  • 1–3 years of experience in test engineering, QA, data analysis, or a related technical role (internship experience counts)
  • Strong analytical and data review skills — comfortable working through large datasets methodically and accurately
  • Python proficiency for data processing, test scripting, and automation tasks
  • BS in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field
  • Must be based in or willing to work on-site in Pittsburgh (lab access required)

Nice to Have

  • Experience with log analysis, telemetry, or sensor data review
  • Familiarity with CI/CD pipelines (GitHub Actions, Jenkins) or test pipeline integration
  • Exposure to computer vision, image processing, or ML model output validation
  • SQL or database querying experience for data extraction and validation