Simulation QA Automation Engineer - Physics, Robotics, & CI

Veridian Tech Solutions, Inc.; United States; On-site; USD 90,000 - 135,000; Full time

Job summary

Veridian Tech Solutions, Inc. seeks a QA Automation Engineer to automate validation of simulation behavior, physics, sensors, scenarios, rendering, and performance.

You will build tests for scene loading, physics behavior, robot motion, sensors, APIs, and generated data, defining baselines and tolerances, and reporting trends to release teams. The role requires experience with PyTest, image-diff methods, statistical validation, or GPU profiling, and collaboration with CI/CD pipelines in GPU test

Qualifications

• Experience in test automation for simulation, robotics, graphics, gaming, data, or scientific software.
• Strong diagnostic and quantitative analysis skills in Python or C++ testing.
• Ability to validate systems where outputs vary within defined tolerances.

Responsibilities

• Create automated tests for scene loading, physics behavior, robot motion, sensors, scenarios, APIs, and generated data.
• Define golden baselines, tolerances, statistical checks, visual comparisons, and regression criteria.
• Test determinism, scalability, performance, stability, error handling, and cross-version compatibility.
• Integrate suites with CI/CD, GPU test environments, defect triage, and release gates.
• Produce clear evidence, diagnostics, and trend reporting.

Skills

Test automation for simulation, robots

Python/C++ testing

Tolerance-based testing

Tools

PyTest

Image diff

Statistical validation

GPU profiling

ROS2 testing

Physics validation

Job description

Veridian Tech Solutions, Inc. seeks a QA Automation Engineer to automate validation of simulation behavior, physics, sensors, scenarios, rendering, and performance.

You will build tests for scene loading, physics behavior, robot motion, sensors, APIs, and generated data, defining baselines and tolerances, and reporting trends to release teams. The role requires experience with PyTest, image-diff methods, statistical validation, or GPU profiling, and collaboration with CI/CD pipelines in GPU test