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QA Engineer - Visual Inspection & Gameplay

Luxoft

Województwo pomorskie

On-site

PLN 180,000 - 240,000

Full time

Today
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Job summary

A technology consulting firm in Poland is seeking a skilled QA Automation Engineer to validate next-generation visual computing technologies. In this role, you will execute manual visual QA sessions and collaborate with teams to enhance automation processes. The ideal candidate possesses strong attention to detail and basic scripting skills. This opportunity is perfect for those passionate about computer graphics and visual fidelity.

Qualifications

  • Ability to spot subtle visual defects.
  • Comfortable with capturing, annotating, and documenting visual artifacts.
  • Willingness to work directly with game clients and production builds.

Responsibilities

  • Carry out manual visual QA sessions across games.
  • Reproduce and document issues with clear reproduction steps.
  • Work with automation engineers to prioritize cases for automation.

Skills

Strong visual attention to detail
Basic scripting ability (Python or shell)
Good communication skills
Job description
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
  • 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.
Skills
Must have
  • Strong visual attention to detail and ability to spot subtle visual defects.
  • Comfortable with capturing, annotating, and documenting visual artifacts (screenshots, videos, frame ranges).
  • Basic scripting ability (Python or shell) to automate repetitive capture tasks and manipulate media files.
  • Good communication skills to describe visual issues to engineering and research teams.
  • Willingness to work directly with game clients and production builds; able to follow test plans and explore ad‑hoc scenarios.
Nice to have
  • Experience with graphics/renderer artifacts and common failure modes (aliasing, temporal instability, lighting issues).
  • Familiarity with video capture tools and codecs, or with in‑engine capture pipelines.
  • Prior exposure to tools like FiftyOne, Voxel51, or model‑based classifiers (CLIP).
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