We are seeking a talented and proactive QA Team Lead with strong programming and tool building skills. The ideal candidate will set up robust validation and inspection systems (both automated and manual) to ensure the highest quality in our data generation and application outputs. This role sits at the intersection of engineering and visual validation, requiring independence, initiative, and deep technical know how in building QA infrastructure and tooling. The Team Lead is expected to be hands on in Python, guide the team in tool development, and evaluate modern ML based media understanding tools such as CLIP and Voxel51.

Own the overall QA strategy for visual media, combining automated and manual approaches to ensure the quality and variance of data sets (generated or existing) through inspection and validation techniques.

Define validation workflows and acceptance criteria for image and video datasets, game scenes, and application outputs.

Lead a small team of QA engineers (manual, automation, data quality) by planning, prioritizing, and distributing work, monitoring progress, and removing blockers.

Build and maintain, together with the team, tooling for fast and effective visual inspection of image and video datasets, including utilities that support pixel based analysis and visual regression.

Oversee the development of internal applications and utilities in Python to automate classification, clustering, and validation of dataset content, plus visualization tools that support efficient manual inspection.

Coordinate the setup and evolution of continuous integration (CI) systems that automate regression testing (for example image comparison and visual regression checks) across graphical outputs and game scenes.

Guide benchmarking efforts where, given a model snapshot, the team identifies and captures sequences showing problems and compares them with state of the art outputs.

Stay updated with and evaluate open source options for dataset classification and visualization (for example CLIP, Voxel51, FiftyOne) and drive adoption of promising solutions in the workflow.

Work closely with engineering, research, and production teams to embed quality check systems into applications and make manual and automated inspections more efficient.

Mentor junior team members in both QA methodology and coding practices (Python or similar), and foster a self driven culture where engineers can set up pieces of infrastructure from scratch.