Senior AI & Synthetic Data Engineer

Tata Consultancy Services Limited; Irving (TX); On-site; USD 90,000 - 150,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Medical Coverage (Medical & Health)

Dental & Vision

Disability Planning & Insurance

Pet Insurance Plans

Maternal & Parental Leaves

Auto & Home Insurance

Identity Theft Protection

Commuter Benefits

Certification & Training Reimbursement

Vacation & Holidays

401K Plan

Job summary

Tata Consultancy Services Limited in Irving, TX is seeking a Senior Engineer to develop synthetic-data pipelines and AI experiments for perception and physical-AI use cases in simulated industrial environments.

You will generate labeled data from simulation, design domain randomization, and train perception models using synthetic, real, and blended datasets, with a focus on reproducibility and scalable releases.

Qualifications

• Experience in computer vision or ML engineering with synthetic-data workflows.
• Proficiency in Python and deep-learning frameworks.
• Understanding of labels, ground truth, dataset bias, evaluation, and reproducibility of experiments.

Responsibilities

• Generate labeled image, video, point-cloud, depth, segmentation, pose, and scene data from simulation.
• Design domain randomization, scenario coverage, class balancing, and dataset versioning.
• Train and evaluate perception models using synthetic, real, and blended datasets.
• Measure domain gap and improve transfer through simulation, augmentation, adaptation, and real data.
• Automate experiments, metadata capture, quality checks, and reproducible releases.

Skills

Computer vision

ML engineering

Python

Deep learning frameworks

Experiment reproducibility

Education

Bachelor of Computer Science

Job description

Tata Consultancy Services Limited in Irving, TX is seeking a Senior Engineer to develop synthetic-data pipelines and AI experiments for perception and physical-AI use cases in simulated industrial environments.

You will generate labeled data from simulation, design domain randomization, and train perception models using synthetic, real, and blended datasets, with a focus on reproducibility and scalable releases.