AI Automation Engineer, Data Annotation Services

Caterpillar•Irving, TX

2d•Onsite

About The Position

At Caterpillar, technology serves the purpose of solving our customers’ toughest challenges. Through Cat Technology, we are solving problems by building the intelligence layer that connects machines, data, and people to make jobsites safer, more productive, and more sustainable. By combining deep domain expertise in physical systems with software, connectivity, autonomy, and AI, we deliver solutions that work in the real world—on real jobsites, at global scale. You’ll build and deploy against one of the most unique data foundations—over 1.6 million connected assets generating real-world data daily. These data and platform capabilities are enabling the development of AI models, edge computing architectures, and software systems that scale across fleets, products, and industries. The result will be a new generation of machines that continuously learn, improve, and deliver performance at scale. Construction autonomy is one of the most complex challenges in applied AI, and at Caterpillar, advancements in physical AI, simulation, sensing, and edge computing are turning things that once felt impossible—intelligent machines operating in dynamic jobsites—into reality. Our connected ecosystem brings together massive volumes of high-quality data to create a foundation where engineers like you can build and deploy against. We are seeking an AI Automation Engineer to develop annotation automation solutions used to create training data for robotics, autonomy, computer vision, and Vision-Language-Action (VLA) systems with a scrum team. This role combines software engineering, machine learning, and data engineering to build automated annotation and captioning capabilities for real-world heavy equipment and construction environments. You will help deploy and extend technologies such as NVIDIA Cosmos Curator and Cosmos Reason while also developing Caterpillar-specific AI models that enable automated labeling, automated captioning, and AI-driven quality assurance. This is a hands-on technical role focused on turning complex AI and data challenges into scalable production solutions.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Robotics, or a related field.
Experience developing production-grade software solutions using Python, modern software engineering practices and containerization platforms (e.g. Docker, 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 in data annotation and quality assurance for autonomous systems or computer vision projects, with demonstrated ability to define annotation guidelines, identify labeling defects, and drive measurable improvements in dataset accuracy.
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 to support auto-labeling, auto-captioning, and annotation quality automation.
Build data pipelines and workflows that move datasets through 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.