AI Automation Engineer, Data Annotation Services

What you'll do

• Develop and deploy automated labeling and captioning solutions for perception and VLA datasets.
• Train, fine-tune, and evaluate AI models to support auto-labeling and annotation quality automation.
• Build data pipelines that move datasets through automated labeling, human review, and quality validation.
• Implement and extend platforms such as NVIDIA Cosmos Curator and other emerging AI technologies.
• Integrate Caterpillar data systems with external annotation providers and human-in-the-loop workflows.
• Ensure reliability, scalability, and operational excellence across all annotation automation services.

What we're looking for

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Robotics, or a related field.
• Experience developing production-grade software using Python and containerization platforms like Docker or Kubernetes.
• Experience designing and implementing distributed systems, APIs, and data processing pipelines.
• Experience training, fine-tuning, or deploying machine learning models.
• Experience generating and managing camera and LiDAR sensor datasets for perception or machine learning applications.
• Experience in data annotation and quality assurance for autonomous systems or computer vision projects.
• Experience using machine learning frameworks such as PyTorch, TensorFlow, ONNX, Weights & Biases, or MLflow.
• Experience with tools like Labelbox, Supervisely, RoboFlow, or similar dataset management toolchains.
• Experience with computer vision, robotics, autonomy, Physical AI, or multimodal AI systems (preferred).
• Experience with AI-assisted annotation, auto-labeling, or machine learning data pipelines (preferred).
• Experience training or fine-tuning foundation models, vision models, or multimodal models (preferred).
• Experience with NVIDIA Cosmos Curator, Cosmos Reason, or similar AI automation platforms (preferred).
• Experience with Vision-Language-Action (VLA) datasets and workflows (preferred).
• Experience with cloud platforms, MLOps, and machine learning deployment pipelines (preferred).
• Experience building human-in-the-loop AI workflows and quality automation systems (preferred).