What you will do

Luxoft, in partnership with AMD, is seeking skilled QA Automation Engineers to join a team dedicated to validating next-generation visual computing technologies at scale. This group builds automated systems that verify visual outputs - images, videos, and real-time game scenes - across a wide range of GPU hardware and rendering pipelines.

As part of the Software QA Test Automation organization, you will design and evolve tooling that performs pixel-accurate and perceptual-based comparisons, automate visual regression workflows, and integrate these systems deeply into CI. You will develop and maintain frameworks for large-scale dataset inspection, automated artifact analysis, and visual quality benchmarking.

In this role, you will collaborate closely with Graphics, ML, and Rendering teams to ensure that cutting-edge GPU features meet the highest quality bar. You will evaluate emerging technologies, prototype validation strategies, and remove barriers to large-scale automated testing. Senior engineers will additionally partner with Dev, QA, and Program Management across new initiatives to identify automation opportunities and drive end-to-end quality improvements.

This position is ideal for engineers passionate about computer graphics, visual fidelity, and automation at scale - and who want hands-on access to state-of-the-art hardware, often before it reaches the market.

• Implement automated validation pipelines for image/video datasets (format checks, variance checks, content classification).
• Develop visual regression tooling (pixel-compare, perceptual metrics, temporal consistency checks).
• Build and maintain visualization dashboards and utilities to accelerate inspection and debugging.
• Create automation to classify, cluster, and tag dataset content (including leveraging pretrained models where useful).
• Integrate automated checks into CI/CD so regressions are detected early and reproducibly.
• Produce reproducible scripts and tooling to capture model snapshots and run benchmark comparisons.
• Collaborate with research/production to codify evaluation criteria and acceptance thresholds.

Requirements

MUST

• Strong programming skills (Python or similar), test automation, and scripting.
• Experience processing and analyzing image/video datasets (frame extraction, codecs, metadata).
• Familiarity with visual comparison techniques (SSIM, LPIPS, perceptual metrics) and how to implement them robustly.
• Experience with CI systems (Jenkins, GitHub Actions, GitLab CI) and automating pipeline steps.
• Good debugging skills and ability to make tooling resilient and maintainable.

NICE TO HAVE

• Experience with state-of-the-art ML models for media understanding (e.g., CLIP) and related open-source toolkits (Voxel51, FiftyOne, etc.).
• Exposure to games or graphics pipelines.
• Familiarity with benchmarking methodologies for AI, vision, or rendering applications.