Lead AI Automation Engineer, Data Annotation Services

Caterpillar Inc.•Irving, TX

3d•$128,470 - $208,770•Onsite

About The Position

We are seeking a Lead AI Automation Engineer to provide technical leadership for a scrum team responsible for developing annotation automation solutions used to create training data for robotics, autonomy, computer vision, and Vision-Language-Action (VLA) systems. 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 leadership role focused on turning complex AI and data challenges into scalable production solutions. You'll work on some of the most challenging applications of AI in the physical world, helping build systems that transform vast amounts of machine and sensor data into the intelligence that powers autonomous and AI-enabled equipment. If you're excited about applying AI to real-world machines operating in complex environments, we'd like to hear from you.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Robotics, or a related field.
Experience leading technical delivery for software or AI engineering teams.
Experience developing production-grade software solutions using Python and modern software engineering practices.
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.
3+ years of experience generating and managing camera and LiDAR sensor datasets for perception or machine learning applications, including data collection pipeline design and ground truth generation.
3+ years of 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

Lead the technical execution of a scrum team focused on annotation automation and AI-driven data production.
Design, 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.
Drive reliability, scalability, observability, and operational excellence across annotation automation services.
Mentor engineers and provide day-to-day technical leadership for the team.