Software QA Engineer - Physics AI

Validate Physics AI capabilities: Work closely with AI/ML engineers to understand the underlying physics models, simulation workflows, and algorithms, and validate the accuracy, robustness, fidelity, and consistency of AI predictions against traditional solver or reference results. Own end-to-end quality: Drive QA activities across the software development lifecycle, independently planning and executing quality activities to ensure reliable delivery of AI-enabled simulation capabilities. Design comprehensive testing: Review user stories, requirements, and design documentation for testability and develop functional, stress, performance, and end-to-end test scenarios aligned with engineering use cases. Develop and maintain test automation: Create, execute, and maintain automated tests using scripting and automation frameworks, while improving existing test suites and porting automation to high-performance systems where appropriate. Investigate complex technical issues: Go beyond identifying defects by reproducing issues, analyzing logs and system behavior, investigating root causes, and collaborating with Development and AI/ML teams to drive resolution. Improve quality engineering practices: Identify potential risks proactively, research complex problems, introduce effective solutions, and champion best practices in software quality, automation, and testing of Physics AI capabilities. Collaborate across engineering teams: Work closely with Development, AI/ML Engineering, DevOps, Product Management, Applications Engineering, and other teams in a globally distributed environment. Qualifications 4 6+ years of relevant technical experience in CAE, engineering simulation, design verification, Physics AI, Engineering AI, or a related domain, along with hands-on experience in Software Quality Engineering or software validation. 1 3+ years of professional experience in Software QA, Quality Engineering, software testing, or test automation, preferably for complex engineering or desktop software products. Strong understanding of CAE and engineering simulation workflows, with the ability to analyze simulation results and assess their technical and physical reasonableness. Exposure to AI/ML or Engineering AI, with the ability to understand and validate AI-driven predictions against physics-based, simulation, or solver-generated reference results. Experience with desktop simulation or engineering software such as Simcenter, CAD/CAE applications, or similar products; experience with HyperView, Simcenter 3D Post, or equivalent post-processing tools is highly valuable. Proficiency in Python, C, or similar scripting/programming languages, with hands-on experience developing or maintaining automated tests using PyTest, Ranorex, Selenium, or comparable frameworks. Strong analytical, problem-solving, communication, and collaboration skills, with the ability to work independently, investigate complex technical issues, and contribute effectively in cross-functional teams. Preferred Skills Experience testing or validating AI/ML models, Physics AI, surrogate models, or AI-enabled engineering applications. Experience comparing AI predictions with traditional CAE/CFD/FEA solver results, experimental data, or other engineering reference data. Familiarity with Windows and UNIX/Linux environments and cross-platform desktop software testing. Experience with JIRA, Xray, Git, and modern test automation frameworks. Experience with functional, regression, stress, performance, and system-level testing. Strong attention to detail, curiosity, self-motivation, and the ability to quickly learn unfamiliar engineering and software technologies. Desired Candidate Profile 4 6+ years of relevant technical experience in CAE, engineering simulation, design verification, Physics AI, Engineering AI, or a related domain, along with hands-on experience in Software Quality Engineering or software validation. 1 3+ years of professional experience in Software QA, Quality Engineering, software testing, or test automation, preferably for complex engineering or desktop software products. Strong understanding of CAE and engineering simulation workflows, with the ability to analyze simulation results and assess their technical and physical reasonableness. Exposure to AI/ML or Engineering AI, with the ability to understand and validate AI-driven predictions against physics-based, simulation, or solver-generated reference results. Experience with desktop simulation or engineering software such as Simcenter, CAD/CAE applications, or similar products; experience with HyperView, Simcenter 3D Post, or equivalent post-processing tools is highly valuable. Proficiency in Python, C, or similar scripting/programming languages, with hands-on experience developing or maintaining automated tests using PyTest, Ranorex, Selenium, or comparable frameworks. Strong analytical, problem-solving, communication, and collaboration skills, with the ability to work independently, investigate complex technical issues, and contribute effectively in cross-functional teams. Preferred Skills Experience testing or validating AI/ML models, Physics AI, surrogate models, or AI-enabled engineering applications. Experience comparing AI predictions with traditional CAE/CFD/FEA solver results, experimental data, or other engineering reference data. Familiarity with Windows and UNIX/Linux environments and cross-platform desktop software testing. Experience with JIRA, Xray, Git, and modern test automation frameworks. Experience with functional, regression, stress, performance, and system-level testing. Strong attention to detail, curiosity, self-motivation, and the ability to quickly learn unfamiliar engineering and software technologies.