QA Automation Engineer
16 godzin temu
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pmironczuk
Project Description:
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.
Responsibilities:
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.
Mandatory Skills Description:
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 Skills Description:
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.
Languages:
English: C1 Advanced
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