AI Automation Engineer, Data Annotation Services

Caterpillar Inc.•Irving, TX

8d•$97,530 - $158,480•Onsite

About The Position

Caterpillar is seeking an AI Automation Engineer to develop annotation automation solutions that create training data for robotics, autonomy, computer vision, and Vision-Language-Action (VLA) systems. Working with a scrum team, this hands-on technical role combines software engineering, machine learning, and data engineering to build scalable automated annotation and captioning capabilities for real-world heavy equipment and construction environments. The engineer will deploy and extend technologies such as NVIDIA Cosmos Curator and Cosmos Reason, and develop Caterpillar-specific AI models for automated labeling, captioning, and AI-driven quality assurance. The role focuses on turning complex AI and data challenges into reliable production solutions, building data foundations for intelligent machines, and supporting safer, more productive operations. This position is based full-time onsite at Caterpillar’s Irving, Texas (Dallas) office, five days per week. Domestic relocation assistance is available, and visa sponsorship is available for eligible applicants. The summary pay range is $97,530 to $158,480.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Robotics, or a related field.
Experience developing production-grade software using Python, modern software engineering practices, and containerization platforms such as Docker or Kubernetes.
Experience designing and implementing distributed systems, APIs, and data-processing pipelines.
Experience training, fine-tuning, or deploying machine-learning models.
Strong problem-solving skills and ability to work across multiple technical disciplines.
Experience generating and managing camera and LiDAR sensor datasets for perception or machine-learning applications, including data-collection pipeline design and ground-truth generation.
Experience with data annotation and quality assurance for autonomous systems or computer-vision projects, including defining annotation guidelines, identifying labeling defects, and improving dataset accuracy measurably.
Experience using annotation and dataset management tools such as Labelbox, Supervisely, RoboFlow, or similar toolchains.
Experience using machine-learning frameworks and training tools such as PyTorch, TensorFlow, ONNX, Weights & Biases, MLflow, or similar frameworks.

Nice To Haves

Experience with computer vision, robotics, autonomy, Physical AI, or multimodal AI systems.
Experience with AI-assisted annotation, auto-labeling, or machine-learning data pipelines.
Experience training or fine-tuning foundation models, vision models, or multimodal models.
Experience with NVIDIA Cosmos Curator, Cosmos Reason, or similar AI automation platforms.
Experience with Vision-Language-Action (VLA) datasets and workflows.
Experience with sensor data such as cameras, LiDAR, radar, or autonomous-machine datasets.
Experience with cloud platforms, MLOps, and machine-learning deployment pipelines.
Experience building human-in-the-loop AI workflows and quality automation systems.

Responsibilities

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