Non-Engineering
Originally posted 29 October 2026 by the employer.
Own quality engineering and automated testing across simulation platforms and synthetic datasets for UR cobots and MiR mobile robots.
About the role
As a QA/QC Engineer on the Simulation team, you will own the quality of simulation software, services, 3D assets, rendering output, and synthetic datasets. Your work supports simulation environments used to evaluate UR cobots and MiR mobile robots.
What you'll do
- Define and own test strategies, test plans, and test cases for simulation software, tools, services, and web applications.
- Build and maintain automated test suites—functional, regression, integration, and performance—integrated into CI/CD pipelines.
- Develop automated quality checks for 3D assets, including geometry, real-world scale, pivots, materials, collision meshes, physics properties, and naming conventions.
- Validate synthetic datasets for label accuracy, annotation consistency, format compliance, and distribution coverage.
- Build image-based and visual regression tests for rendering and sensor simulation output, and verify simulated robot behaviour against real hardware.
- Define acceptance criteria and quality benchmarks for deliverables from third-party asset vendors, reviewing incoming work against standards.
What you'll need
- Bachelor's degree in Computer Science, Engineering, or a related technical field.
- 3+ years of professional experience in software QA, test automation, or quality control.
- Strong programming proficiency in Python for building automated tests and tooling.
- Hands-on experience with test frameworks such as pytest, Playwright, or Selenium.
- Experience integrating tests into CI/CD pipelines such as GitHub Actions, GitLab CI, or Jenkins.
- Solid understanding of QA methodologies, the software development lifecycle, defect-tracking tools such as Jira, and working in a Linux environment.
- Proficiency in at least four domain areas: 3D applications or simulators; 3D formats and tools (USD, FBX, glTF, Blender, Maya, Houdini); computer vision dataset validation (COCO, KITTI, YOLO); visual regression (OpenCV, perceptual diffing); robotics software or ROS2; performance/load testing of GPU workloads; REST/GraphQL API testing; Docker; pandas or NumPy; or ISTQB certification.
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