Description

Responsibilities

• Build automation for sensor-data ingestion, curation, sampling, annotation preparation, validation, and

release.

• Train, fine-tune, evaluate, and support deployment of machine learning models for computer vision or

perception use cases.

• Design camera and LiDAR data collection and ground truth generation pipelines.

• Define annotation guidelines, acceptance criteria, and automated checks for label completeness,

consistency, and accuracy.

• Analyze labeling defects and model-error patterns, then implement corrective actions and measurable

quality improvements.

• Create reporting for dataset health, annotation quality, model performance, and workflow throughput.

• Collaborate with data engineering, annotation operations, quality, and AI teams

• Demonstrated experience training, fine-tuning, and deploying machine learning models.

• Demonstrated experience generating and managing camera and LiDAR sensor datasets for

perception or machine learning applications.

• Experience designing data collection pipelines and ground truth generation workflows.

• Demonstrated experience in data annotation and quality assurance for autonomous systems or

computer vision projects.

• Demonstrated ability to define annotation guidelines, identify labeling defects, and deliver measurable

improvements in dataset accuracy.

• Strong Python skills and experience automating data validation, model evaluation, and machine

learning workflows.

• Understanding of computer vision datasets, model metrics, experiment tracking, version control, and

deployment practices

Key Skills

Education

Any Gradute