AI Automation Engineer

Veridian Tech Solutions, Inc.; Dallas (TX); On-site; USD 85,000 - 120,000; Full time

Job summary

Veridian Tech Solutions, Inc. is seeking a data-focused ML engineer to train, fine-tune, and deploy computer-vision models.

You will design data collection pipelines for camera and LiDAR, generate ground truth, and establish robust annotation guidelines to ensure high-quality datasets. You will collaborate with data engineering and AI teams, automate validation and evaluation, and report on dataset health and model performance.

Qualifications

• Demonstrated experience training, fine-tuning, and deploying machine learning models.
• Experience designing data collection pipelines and ground truth generation workflows.
• Demonstrated experience in data annotation and quality assurance for autonomous systems or similar.
• 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 tasks.

Responsibilities

• Collaborate with data engineering, annotation operations, quality, and AI teams.
• Create reporting for dataset health, annotation quality, model performance, and workflow throughput.
• 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 tasks.
• 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.

Skills

Python

Data annotation

Ground truth

Dataset management

Model evaluation

Automation

CV datasets

Experiment tracking

Version control

Deployment

Tools

Git

Job description

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