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.

• Carry out manual visual QA sessions across games and graphical scenes to validate model or renderer outputs.
• Play through sequences, record and annotate visual artifacts, temporal glitches, and regressions.
• Use and provide feedback to visualization tools to speed manual inspection (frame viewers, side-by-side comparisons).
• Reproduce and document issues for engineering teams with clear reproduction steps, artifacts, and metadata.
• Provide labeled examples and curated sequences for benchmark suites and automated tests.
• Work with automation engineers to prioritize cases for automation and help tune detection thresholds.
• Assist in dataset curation and sanity checks for training and evaluation datasets.