Our QA pipeline rejects the mechanically impossible before humans ever see it. You will design and extend those validators — geometry checks, distribution monitors, agreement analytics — and wire them into the annotation workflow.
Machine Learning Quality Engineer
About the Role
Responsibilities
- Design automated validation rules for image, video, and point-cloud annotations
- Build inter-annotator agreement dashboards and drift alerts
- Profile and optimise batch-processing performance
- Collaborate with pod leads to convert recurring error patterns into validators
- Maintain CI for the QA tooling codebase
Requirements
- 2+ years in Python-based data engineering or ML tooling
- Solid grasp of computer-vision data structures (masks, cuboids, point clouds)
- Experience with NumPy/pandas and at least one deep-learning framework
- Comfort reading annotation-format specs (COCO, KITTI, DICOM)
- Autonomous remote-work discipline
Skills
PythonNumPy / pandasComputer vision formatsData pipelinesStatistical QA methods
Benefits
- Fully remote with flexible hours
- Hardware budget
- Conference and course allowance
- Health coverage
- Annual performance bonus